The role of extreme investor sentiment on stock and futures market. returns and volatilities in Taiwan

Transcription

1 The role of exreme invesor senimen on sock and fuures marke reurns and volailiies in Taiwan Hsiang-Hsi Liu Graduae Insiue of Inernaional Business Naional Taipei Universiy Chun-Chou Wu Deparmen of Finance Naional Kaohsiung Firs Universiy of Science and Technology Yi-Kai Su Graduae Insiue of Finance Naional Taiwan Universiy of Science and Technology JEL codes: G1, G1. Corresponding auhor. Address: Jhuoyue Rd., Nanzih, Kaohsiung Ciy 811, Taiwan, R.O.C. Tel: ex. 015 Fax:

2 The role of exreme invesor senimen on sock and fuures marke reurns and volailiies in Taiwan Absrac This sudy uses differen volailiy models o describe he condiional volailiy paern and incorporaes exreme senimen indicaors ino he models for he dynamic srucure of reurns. This research ries o design differen grades for abnormal rading volume as a proxy for exreme invesor senimen o deec he relaionships beween exreme invesor senimen and marke reurns. Meanwhile, he direc and indirec effecs of hese senimenal facors on marke reurns are examined. The empirical resuls clearly indicae ha he exreme brigh and dark senimen indicaors have various effecs on marke reurns. Addiionally, we also discuss he impacs of he Financial Tsunami on reurns and volailiy srucure. We can infer ha he exreme senimen indicaor sill plays a criical role in exploring changes in marke reurns. Finally, incorporaing he specific senimen indicaors in he shor and long volailiy srucures would have an indirec influence on marke reurns. Keywords: Abnormal Trading Volume, Exreme Senimen Indicaors, Componen Volailiy

3 1. Inroducion The senimen indicaor for invesors has been acceped as a key ook for analyzing change in marke reurns. Several prior sudies sae ha rading volume could be viewed as a proxy of he invesor senimen facor and furhermore ha he abnormal rading volume could also be considered an irraionally senimenal reacion. Neverheless, he ransmission mechanism beween he laen senimen indicaors and marke reurns are sill ambiguous. In his paper, we documen ha exreme invesor senimen drives he marke reurns in direc and indirec ways. We use he mos represenaive financial markes, Taiwan s securiies markes, o discuss his subjec, as in hese markes individual raders occupy more han 70% of he rading volume. The influences beween noise rade and expeced reurns were firs consruced by De Long, Shleifer, Summers, and Waldmann (1990, hereafer DSSW). In pracice, we rea invesor senimen as he proxy of noise raders behavior. Based on heir argumen, invesor senimen could affec he expeced reurns in boh ransiory and permanen ways. In general, he ransiory influence can be classified as he hold-more and he price-pressure effecs ha indicae he direc influences of noise raders aciviies on heir expeced reurns, which are simulaneously ascribed o variaions in invesor senimen. The permanen form can be divided ino wo sub-ypes, namely, he Friedman and he creae-space effecs. The permanen effec can indicae he indirec influences of noise raders aciviies on heir expeced reurns, which also follow he acion of variaions in senimen on he volailiy of reurns. This paper offers some ineresing insighs for he exreme senimen indicaor (hereafer ESI) ha may have salien influences on marke reurns hrough direc and indirec processes. 3

4 In order o avoid offseing by he insignifican inerference of rivial senimen from non-exreme rading volume, i is reasonable o exrac he exreme rading volume as a proxy for he laen senimen index. Baker and Sein (00) poin ou ha he high rading volume reflecs high invesor senimen and leads o low expeced reurns. Baker and Wurgler (007) and Hong and Sein (007) have analogous viewpoins ha rading volume can also be used as a proxy for invesor senimens. However, o our knowledge, he ineracions among rading volume, volailiy and marke reurns remain unclear for sock markes and fuures markes. I is inuiive o classify he quaniy of rading volume, using volumes greaer or smaller han a sandard deviaion from he mean for a ime inerval as differen measuremens of ESI. Reail raders are usually much more concerned abou he occurrence of exreme rading volume han are insiuional raders. Kumar and Lee (006) find ha he rading aciviies of reail raders can be viewed as a proxy for he senimen facor. Generally speaking, as he rading volume becomes abnormal, invesors will show much more expecaion in rading behavior han hey will in normal rading siuaion. Thus, i is quie naural o decompose differen rading grades o denoe differen senimen responses. We conjecure ha he various senimen responses will have differen effecs on expeced reurns hrough direc and indirec behaviors. Again, Baker and Wurgler (007) conclude ha he differen grades of senimen will reflec asymmeric average reurns on differen ypes of socks. Baker and Sein (00) and Barber, Odean and Zhu (009) argue ha abnormal rading volume can be considered a signal of irraional invesor senimen. Based on hese references, his research infers ha he abnormal rading volume can be inroduced as an ESI for discussing he change of marke reurns. Brown (1999) proposes ha he asse volailiies are affeced parly by invesor senimen. I seems ha invesor senimen will affec asse reurns direcly or hrough inerim volailiy o affec asse reurns. The componen volailiy

5 model proposed by Engle and Lee (1999) is among he appropriae approaches for dissecing his ineresing subjec. The componen volailiy model helps o decompose he condiional volailiy ino long- and shor-erm componens ha can aid in he discussion of he ransiion beween reurns and is volailiy. Addiionally, his paper will coninue o fi he ESI ino mean, long- and shor-erm volailiy srucures, and i will aemp o describe he influence hrough such a rigorous seing. Lee, Jiang and Indro (00) sae ha he invesor senimen can cause a shock in boh he formaion of condiional volailiy and expeced reurns approved from hree marke indices, namely, DJIA, S&P500, and NASDAQ. 1 Furhermore, Lee, Jiang and Indro (00) also incorporae boh bullish and bearish senimens ino heir model for discussing he condiional volailiy srucure. Meanwhile, fiing he condiional variance ino he mean equaion and hen inferring his parameer can explain is role in mediaing influence. Indeed, many relaive lieraures have agreed ha he componen volailiy model proposed by Engle and Lee (1999) could capure more compleely dynamic process and perform well in model fiing for financial marke volailiy. These findings offer a meaningful, workable direcion for using he componen volailiy model in exploring he relaionships among marke reurns, volailiy and invesors senimen responses. This sudy aemps o examine he ESI as an influence on marke reurns hrough direc and indirec processes in hree major financial markes in Taiwan, namely, he TAIEX, he TAIFEX and OTC (over-he-couner) markes. One of he noiceable properies is ha he proporion of average individual invesor rading volume is found o be abou 76.8% in TAIFEX, 7.3% in TAIEX and 87.3% in OTC during he invesigaive period. One of he purposes for his paper is rying o assis raders in finding significan facors in asse price 1 Lee, Jiang and Indro (00) selec he Invesors Inelligence of New Rochelle senimen index as he proxy for a senimen facor. Engle and Lee (1999), Fleming, Kirby, and Osdiek (008), and Adrian and Rosenberg (008) show ha he componen volailiy model performs well in he sock marke. Chrisoffersen, Jacobs, and Wang (006) find ha use of he componen volailiy model price opions could increase he accuracy. 5

6 discovery, arbirage and hedging in Taiwan financial markes. Our empirical resuls can explicily show he influence of he ESI on marke reurns. The remainder of his aricle proceeds as follows. Secion inroduces he daa properies and discusses he senimen indicaor. Secion 3 describes he main empirical resuls. Secion discusses he Financial Tsunami of 008 and is impacs on he ESI. The las secion provides some concluding remarks.. Daa sources and senimen indicaors In Taiwan s financial markes, he individual raders are he major paricipans for liquidiy rading. Therefore, i is suiable o dissec he ineracion for senimen indicaors and marke reurns based on previous saemens. The main daa for his aricle include rading volumes and marke reurns for he TAIEX, he TAIFEX and he OTC marke, which are colleced from TEJ. The research period is from January 3, 001 o May 7, 009. Daily daa are colleced in order o obain he esimaion (also see Meron, 1980). To consruc he ESI, his sudy exracs he values for rading volumes ha are smaller or greaer han one sandard deviaion from he mean, i.e., hose values ha are greaer han one sandard deviaion from he mean can be regarded as a proxy for an exreme brigh senimen indicaor (hereafer EBSI), and hose ha are smaller han one sandard deviaion from he mean can be a proxy for he exreme dark senimen indicaor (hereafer EDSI). The purpose is o poin ou he relaionship beween he differen grades of abnormal rading volumes and he influence of various exreme senimens. Thus, i is suiable o ake he exreme senimens as laen signals o discuss heir influence on changes in marke reurns. The daily 6

7 reurns are calculaed from he daily daa for closed prices on TAIEX, TAIFEX and OTC. The marke reurns are defined as follows. close Reurns of TAIEX = 100 [ln( P ) ln( P 1 close close Reurns of TAIFEX = 100 [ln( F ) ln( F 1 close Reurns of OTC = 100 [ln( O ) ln( O 1 close )] close )] )] close close where denoes he TAIEX closing price a ime, denoes he TAIFEX closing P close price a ime, and denoes he OTC closing price a ime. O F Descripive saisics for hese daily marke reurns and rading volume are repored in Table I. The average daily marke reurns for he whole sample period are % for TAIEX, % for TAIFEX, and % for OTC, respecively. The values for he maximum and minimum reurns for he sock markes show ha he exisence of seven percen upper and lower bounds as a resul of governmen regulaion. All marke reurns and rading volumes are apparenly no following a normal disribuion by Bera-Jarque crierion. Furhermore, he kurosis for all marke reurns exhibis a fa-ailed shape. A firs glance, he GARCH family model seems appropriae o fi hese rading daa. [Table I] The relaionship beween invesor senimen and marke reurns is discussed below. This sudy specified wo classificaions for rading volume, scaled volume and deviaed volume, as 7

8 proxies for invesor senimens. 3 Using a simple regression model can roughly express he relaions beween invesor senimens and marke reurns. R c1 + cs + ε =, (1) where is he daily marke reurns for TAIEX, TAIFEX, and OTC, is he invesor R i, senimen proxy for he ih marke a ime ha can be replaced by scaled volume or deviaed S volume, and ε is he error erm based on he regression model. Panel A and panel B of Table II repor he esimaed resuls of MLE regression for hree differen markes. The coefficien for c shows ha he scaled volume evidenly has a posiive influence on spo marke reurns and ha he deviaed volume has significanly negaive influence for all marke reurns. These resuls are no quie explici enough o compleely describe he influence of invesor senimen on he change of marke reurns. I seems ha he scaled volume conains some noise informaion and ha he resuls implicily show an influence on marke reurns. In order o ouline he influence of invesor senimen on rading volume, his sudy selecs he deviaed volume variable o represen he effecive senimen indicaor for empirical discussion laer. A special specificaion is o separae he deviaed rading volumes ino wo pars denoing he brigh and dark senimen indicaors. The ESI in such a model srucure can easily describe he differen aspecs of invesors moods. Thus, below is he modified model based on Eq. (1) which decomposes he senimen variable for he ESI ino wo pars R c1 + cs _ H + c3s _ Li, + ε =, () where _ and _ represen EBSI and EDSI for raders respecively. Again, he S H i, S L i, regression resuls are shown in Table II. For he coefficiens of c and c 3 on Panel C, hey are 3 Le he variable for rading volume be q. The scaled volume can be calculaed by using he sandardizaion of he rading volume (Fleming e al., 008), he scaled volume can be expressed as follows. Scaled volume equals [q-e(q)] / SD(q). The variable for deviaed volume is formed by soring ou he values of rading volume ha are greaer or smaller han one sandard deviaion of he mean and defined as follows. Deviaed volume = max{q-[e(q)+sd(q)],0} abs{min{q-[e(q)-sd(q)],0}}. In his paper, we define deviaed volume as he abnormal rading volume. 8

9 mosly significan as depicing ha he ESI explicily affecs marke reurns. The EBSI and EDSI represen differen marke moods for raders. Firs, he specificaion of invesor senimen is quie imporan unsuiable specificaion may lead confused resuls. Secondly, he dark senimen (c 3 ) has a negaive influence for all marke reurns. The brigh senimen (c ) generaes posiive marke reurns for he spo markes. However, i has a negaive influence for fuures marke reurns. Furhermore, he magniude of negaive influence is greaer han ha of posiive influence, according o Panel C, Table II. This finding also explains why a negaive influence can dominae all marke reurns hrough deviaed volume on Panel B, Table II. Basically, hese debaable empirical resuls offer conribuions for incorporaing invesor senimen indicaors o invesigae marke reurn behavior. [Table II] In Figure 1, he ESI for TAIEX, TAIFEX and OTC are respecively graphed. In order o clearly disinguish he EBSI and EDSI, we use a negaive quaniy o denoe he EDSI. I is apparen ha he same aspec of ESI roughly displays clusering. This finding suppors ah he ESI derived from abnormal rading volume will be a criical role in he change of marke reurns. [Figure 1] 3. Model seup and he effecs for ESI o reurns and volailiies Due o he kurosis coefficien s being greaer han hree for reurn processes in Table I, i is reasonable o adop GARCH(1,1) model in order o esimae and check he relaionship 9

10 beween ESI and he volailiy of marke reurn. Laer, we discuss he direc and indirec impacs of ESI for differen marke reurns. Finally, he componen volailiy model proposed by Engle and Lee (1999) is inroduced o describe he complee dynamic volailiy process. Meanwhile, we analyze he deailed influences on marke reurns hrough wo disinc ESI respecively. The GARCH model has been exensively cied and analyzed. Bollerslev, Chou and Kroner (199) sugges ha he GARCH(1,1) model is he mos parsimonious volailiy model o fi mos financial daa. Based on such a survey, his sudy applies he GARCH(1,1) model o fi marke reurn processes in he beginning. The mean equaion is represened by Eq. () previously. The error erm in Eq. () ε N(0, h ), and he condiional volailiy equaion can be shown as h ~ = ωi + αiε 1 + βihi, + θ1s _ H + θs _ Li, 1, (3) where is he condiional volailiy and he coefficiens h i, 1 θ and θ represen he influences for he EBSI and EDSI o condiional volailiy. We also consider he possible influence from volailiy effec o reurns. Afer incorporaing he volailiy erm ino Eq. (), he mean equaion can be modified as R c1 + cs _ H + c3s _ Li, + chi, 1 + ε =, () where c denoes he magniude of indirec influence from ESI shocks. Table III repors he empirical resuls for he simple GARCH(1,1) model and he GARCH-in-mean model wih he influence of ESI. Panels A and B of Table III provide GARCH(1,1) esimaed coefficiens indicaing ha he ESI has a subsanial influence on The risk premium erm esablished on GARCH-in-mean model could cie lagged condiional volailiy (Brooks, 00, p.80). 10

11 boh marke reurns and condiional volailiy for all markes. Such an inference can be mosly be confirmed by he esimaes for c, c 3, θ 1 and θ. One of our findings is ha he direc influence of EDSI is noiceably conrolled by he price-pressure effec for all markes and ha he magniudes are for he TAIEX, for he TAIFEX, and for OTC on Panel A of Table III. However, he direc influence for EBSI is dominaed by he hold-more effec in he spo markes and is dominaed by he price-pressure effec in he fuures marke. The sizes of he direc influence for EBSI are 0.9 for he TAIEX, 0.33 for OTC, and for he TAIFEX on Panel A of Table III. These resuls no only suppor ha he GARCH volailiy model is suiable for fiing marke rading daa bu also convincingly poin ou ha i is appropriae o incorporae he risk premium erm riggered by ESI. Neverheless, he ESI s effec on risk premium seems insignifican on Panel C of Table III for hese hree markes. These findings are in agreemen wih previous lieraures ha he compensaions of risk bearing are ofen hard o recognize based on a ime-series framework. 5 In order o obain more complee blueprin for hese relaionship, we use componen volailiy model o deec he ESI s effecs on he risk premium. [Table III] The componen volailiy model proposed by Engle and Lee (1999) could subsanially decompose he condiional volailiy ino long- and shor-erm componens. This approach can assis analyss o realize he complex process for condiional volailiy and he dynamical relaionship beween risk and reurn. Thus, his sudy ries o make a few modificaions o he radiional componen volailiy model for performing he research purpose. The ad hoc specificaion model is presened below: 5 Also see Baillie and DeGennaro (1990), Glosen, Jagannahan, and Runkle (1993), and Guo and Whielaw (006). 11

12 R = c1 + cs _ H + c3s _ Li, + cqi, 1 + c5( hi, q ) + ε, (5) q = ωi + ρiq 1 + ϕi( ε hi, ) + θ1s _ H + θs _ Li, (6) h = q + αi( ε 1 q ) + βi( hi, q ) + θ3s _ H + θs _ Li,. (7) where q i, 1 is he long-erm volailiy and can be viewed as he uncondiional variance, and hi, q i, 1) represens he shor-erm volailiy. The coefficiens c and represen he ( 1 influence of he EBSI and he EDSI, respecively. These influences can be viewed as he direc effecs of he ESI on reurns. Namely, hese are he direc effecs of exreme senimens on reurns. Addiionally, he esimaed coefficiens c and c are wo exhibied risk premia brough hrough long- and shor-erm volailiies, respecively. The risk premia can be regarded as indirec effecs when he ESI shows subsanial performance under he componen volailiy srucure. Based on equaions (6) and (7), if he ESIs are saisically significan for eiher long- or shor-erm volailiy, hen volailiy predominaely impacs marke reurns. A he same ime, i means ha he indirec influence coming from senimen indicaors exiss. 5 c 3 One of he advanages of using he componen volailiy model o express he indirec influence is ha we could ge meaningful informaion such as he effec of shor- and long-erm volailiy on marke reurns. On he conras, he direc effec includes permanen (long-erm) and ransiory (shor-erm) volailiy informaion. I is necessary o realize he precise indirec influence, as he differen inermediaries would bring dissimilar resuls. When an ESI affecs marke reurns hrough permanen volailiy, i shows in agreemen wih marke reurns responding o he ESI hrough pas volailiy. Meanwhile, marke reurns reac o he ESI hrough unexpeced shocks lagged only one period. Applying more rigorous economeric ess, we calculae he model selecion crierion wih he Bayesian informaion crierion (BIC) 1

13 o compare he performance beween GARCH-in-mean and componen volailiy models. 6 According o his model selecion crierion, he BIC values for GARCH-in-mean models are for he TAIEX,.183 for he TAIFEX, and.111 for OTC, bu he componen volailiy models are 3.87 for he TAIEX,.10 for he TAIFEX and.00 for OTC. Therefore, we can infer ha he componen volailiy model performs more ruly han he GARCH-in-mean model from he empirical resuls. Three model specificaion empirical resuls are shown in Table IV. The volailiy componen equaions including boh he EBSI and he EDSI are repored as specificaion 1. Firs, he direc influences on marke reurns hrough he ESI are sill saisically significan. The magniudes of he direc influence of EBSI are 0.55 for he TAIEX, for he TAIFEX, and for OTC, bu hose of EDSI are for he TAIEX, for he TAIFEX, and for OTC. This finding is consisen wih our previous empirical oucome confirming ha negaive (posiive) marke reurns are riggered by EDSI (EBSI) for spo markes. However, simply negaive marke reurns are induced by boh EDSI and EBSI in he fuures marke. In oher words, dark senimen is dominaed by a price-pressure effec for all markes, and brigh senimen is dominaed by a hold-more (price-pressure) effec in he spo (fuures) marke. As suggesed by DSSW(1990), he hold-more effec suggess ha he noise raders are compensaed for bearing more risk by holding more risky asses relaive o he arbirageurs. The price-pressure effec resuling from he overreacion of asse prices reduces he expeced reurns. Secondly, boh he EBSI and he EDSI approximaely exhibi significan effecs in he long- and shor-erm volailiy equaions. This finding suppors ha he componen volailiy model performs more comprehensively han he GARCH-in-mean model for marke rading daa. Moreover, he componen volailiy model could avoid he 6 GARCH-in-mean and componen volailiy model are non-nesed. Adrian and Rosenberg (008) poin ou ha using BIC o compare models is applicable, as models are non-nesed. 13

14 offseing for he indirec influence of ESI. The indirec influence is revealed on he spo markes hrough shor-erm volailiy he sizes are 0.75 for he TAIEX and 0.76 for OTC bu on he fuures marke hrough long-erm volailiy, he size is 0.0. I seems o demonsrae ha he indirec influence on marke reurns is subjec o he enirey of he ESI. I is of ineres o prudenly group he single-aspec ESIs in he componen volailiy equaions for inspecing wheher he ineracion of indirec influence originaing from he whole ESI exiss. [Table IV] We can focus on he effec of he disinguishable ESI, relaing i o he differen degrees of marke reurns wih volailiies. Furhermore i is reasonable o esimae he real rading daa for hree markes by aking a single ESI in he componen volailiy equaions. The empirical resuls of he model specificaions and 3 are repored in Table IV. Even when we divide he whole ESI ino wo pars and proceed o reconsider he influences of ESI, he esimaed resuls of direc influences on marke reurns hrough ESI are quie similar o hose repored on Table III. These saisical resuls suppor ha he model seing used for his sudy is relaively seady. We discover ha he impacs of shor-erm volailiy affeced by ESI have been changed, especially for TAIFEX and OTC. On he Panel C of specificaion he esimaed coefficien θ 3 becomes posiive and significan for he TAIFEX, and for specificaion 3, he esimaed coefficien θ becomes negaive and insignifican for OTC. In addiion, he esimaions of indirec influence on hese hree marke reurns also exhibi an apparen dispariy. Generally speaking, he sizes of indirec influence on specificaion are for he TAIEX, for he TAIFEX, and 0.79 for OTC; bu hose for specificaion 3 are for he TAIEX, and for he TAIFEX. We go a hin ha he ineracions 1

15 generaed by he ESI in componen volailiy equaions exis. Furhermore, his ineracion may cause a biased esimaion of indirec influence for marke reurns, especially for spo markes. The inermediary influence on marke reurns is purely ha of shor-erm volailiy for spo markes bu is simply ha of long-erm volailiy for he fuures marke. This finding no only agrees wih model specificaion 1 on Table IV bu also highlighs he fine-moving process. As shown on Table IV, he indirec influence of ESI is apparenly affeced by differen aspecs of ESI for hese hree markes. Moreover, he indirec influence of EBSI is approximaely dominaed by a creae-space effec in hese hree markes, bu he indirec influence of EDSI is dominaed by a creae-space effec for TAIFEX and by a Friedman effec for TAIEX. The shor-erm componen can be inerpreed as having ransiory volailiy driven by insan marke shocks. On he conrary, he long-erm componen is saed as he level of risk over a long period. Therefore, we demonsrae ha he indirec influence of ESI only affecs shor-erm volailiy for spo markes. Alhough he ESI can parly affec he esimaion of long-erm volailiy, his impac canno be ransmied o spo-marke reurns. Since he ESI can deeply affec he spo marke reurns, he indirec influence of ESI is manifesed in he long-erm volailiy in he fuures marke. [Figure ] In Figure, we plo he uncondiional volailiy series and scaled rading volume for hose hree differen markes. I is apparen ha he paern of rading volume and uncondiional volailiy are approximaely co-movemens. The phenomenon of co-movemen explains ha he moving of uncondiional volailiy follow he process of senimen. This 15

16 resul is in line wih he oucome in panel B of Table IV; i plos hree differen specificaions simulaneously. I is obvious ha ha he signs of he esimaes are he same bu ha he magniudes are differen. Las, we discover ha he oscillaion in uncondiional volailiy for in fuures marke is larger han hose in wo spo markes (TAIEX and OTC). This resul is consisen wih he descripive saisics on Table I.. The impacs for he 008 Financial Tsunami The financial crisis occurring during was ignied by he Financial Tsunami in Sepember These dramaic financial episodes no only caused many global financial insiuions o collapse bu may have brough he changes in marke reurns and volailiy. Therefore, exploring and re-esimaing he sub-period, January 3, 001-Augus 31, 008, excluding he Sepember 008 Financial Tsunami is necessary. In order o discuss he influence of he ESI and offer a clear comparison wih Table IV, we evaluae he hree specificaions of he componen volailiy model as in he previous arrangemen. The empirical resuls are repored on Table V. Before he Financial Tsunam he indirec influence and he esimaed coefficiens θ and θ have noable changes beween specificaion 1 and oher specificaions for all markes. These resuls show ha he ineracion of specificaion 1 of Table V does exis and also is consisen wih he findings of Table IV. The nex sep is o compare he resuls on Table IV and Table V and discussing he impacs of he Financial Tsunami. On Table V, he direc influence of he ESI on marke reurns is roughly consisen wih he esimaions over he whole period. Neverheless, he indirec influence for model specificaions 1 and 7 Preson (009) has clear discussion on he dae of financial sunami. 16

17 dominaed by he creae-space effec brings he obvious changes in fuures marke reurns. In oher words, medium-erm volailiy is replaced by shor-erm volailiy for he fuures marke. Overall, he medium affecing marke reurns is jus shor-erm volailiy on specificaion for all markes; he magniudes of he effecs are 0.93 for he TAIEX, for he TAIFEX, and for OTC. On specificaion 3, he medium is simply shor-erm volailiy for he TAIEX marke, and he magniude is -0.7; he medium is purely long-erm volailiy for he fuures marke, and he magniude is Only he indirec influence of EDSI is dominaed by he Friedman effec for he TAIEX marke. All in all, he ESI sill plays a significan role in esimaing he marke reurns in direc and indirec ways. Furhermore, he Sepember 008 financial sunami has indeed changed he indirec influence of EBSI on he condiional volailiy, especially in he case of he fuures marke. [Table V] 5. Conclusion In ligh of behavioral finance heory, invesor senimen indeed becomes noiceable in evaluaing expeced reurns. Following DSSW (1990) proposal ha he four effecs generaed by noise rader risk could inerfere wih expeced reurns, i seems o show ha he individual invesing aciviies are endowed wih he abiliy for pricing. These four effecs can be generalized in wo pars, direc and indirec effecs. The explanaions of hese effecs in he previous paper appear imperfec. This paper aemps o employ he componen volailiy model o cope wih he process of hese effecs in is enirey. In addiion, we selec he abnormal rading volume o invesigae and rea he abnormal rading volume as a signal leading he variey of marke reurns. In our specificaion, we define he abnormal rading volume as he ESI and hen separae hem ino EBSI and EDSI. We find ha he ineracion 17

18 beween he EBSI and EDSI really exiss and ha i can cause a biased esimaion of is mediaing influence on marke reurns; herefore we esimae wo differen aspecs of ESI hrough he componen volailiy model. The main empirical finding is ha he EDSI direcly affecs marke reurns for all markes hrough a price pressure effec. However, he EBSI direcly affecs marke reurns hrough hold more effec on spo markes han on he fuures marke. We deec ha he inermediary indirecly affecing marke reurns is he shor-erm volailiy for spo markes bu is he long-erm volailiy for fuures marke, alhough mos of he ESIs have a significan influence on boh long- and shor-erm volailiy. The findings explain ha exreme invesor senimen ruly has a direc and indirec influence on marke reurns and ha he magniude depends on differen facors. We consider ha exreme invesor senimen belongs o insananeous conagious shocks; herefore, he indirec effec is shown hrough shor-erm volailiy for spo markes. Moreover, his phenomenon appears o be he reverse for fuures marke reurns. We believe ha i could be concluded ha he characerisics of he fuures marke are differen from hose of he spo marke. Thus, he indirec effec is shown as being long-erm volailiy in he fuures marke. Finally, we also show he empirical resuls before Sepember 008, he dae of he Financial Tsunami. Aferward, we invesigae he impac of his episode. Our main finding is ha he mediaing influence of he EBSI clearly varies from shor-erm volailiy o long-erm volailiy for he fuures marke. Overall, he ESI sill plays a significan effec on he mean and volailiy of marke reurns in wo disinc ways. 18

19 BIBLIOGRAPHY Adrian T., & Rosenberg, J. (008). Sock reurns and volailiy: Pricing he shor-run and long-run componens of marke risk. Journal of Finance, 63, Baillie, T. R., & DeGennaro, R. P. (1990). Sock reurns and volailiy. Journal of Financial and Quaniaive Analysis, 5, Baker, M., & Sein, J. (00). Marke liquidiy as a senimen indicaor. Journal of Financial Markes, 7, Baker, M., & Wurgler, J. (007). Invesor senimen in he sock marke. Journal of Economic Perspecives, 1, Barber, B. M., Odean, T., & Zhu, N. (009). Sysemaic noise. Journal of Financial Markes, 1, Bollerslev, T. (1986). Generalized auoregressive condiional heeroskedasiciy. Journal of Economerics, 31, Bollerslev, T., Chou, R., & Kroner, K. (199). ARCH modeling in finance: A review of he heory and empirical evidence. Journal of Economerics, 5, Brooks, C. (00). Inroducory economerics for finance (1s ed.). Cambridge: Cambridge Universiy Press. Brown, G.W. (1999). Volailiy, senimen, and noise raders. Financial Analyss Journal, 55, Chrisoffersen, P., Jacobs, K., & Wang, Y. (006). Opion valuaion wih long-run and shor-run volailiy componens (working paper). McGill Universiy. De Long, J. B., Shleifer, A., Summers, L.G., & Waldmann, R. J. (1990). Noise rader risk in financial markes. Journal of Poliical Economy, 98, Engle, R. F. (198). Auoregressive condiional heeroscedasiciy wih esimaes of he variance of U.K inflaion. Economerica, 50,

20 Engle, R. F., & Lee, G. J. (1999). A permanen and ransiory componen model of sock reurn volailiy. In R. F. Engle, & H. Whie (Eds.), Coinegraion, causaliy, and forecasing: A fesschrif in honor of Clive W.J. Granger. Oxford: Oxford Universiy Press. Fleming, J., Kirby, C., & Osdiek, B. (008). The specificaion of GARCH models wih sochasic covariaes. Journal of Fuures Markes, 8, Glosen, L. R., Jagannahan, R., & Runkle, D.E. (1993). On he relaion beween expeced value and he volailiy of he nominal excess reurn on sock. Journal of Finance, 7, Guo, H., & Whielaw, R. (006). Uncovering he risk-reurn relaion in he sock marke. Journal of Finance, 61, Hong, H., & Sein, J. (007). Disagreemen and he sock marke. Journal of Economic Perspecives, 1, Kumar, A., & Lee, C.M. (006). Reail invesor senimen and reurn comovemens. Journal of Finance, 61, Lee W.Y., Jiang, C.X., & Indro, D.C. (00). Sock marke volailiy, excess reurns, and he role of invesor senimen. Journal of Banking and Finance, 6, Meron, R.C. (1980). On esimaing he expeced reurn on he marke: An exploraory invesigaion. Journal of Financial Economics, 8, Preson, P. (009). The oher side of he coin: Reading he poliics of he 008 financial sunami. Briish Journal of Poliics and Inernaional Relaions, 11, Shleifer, A. (000). Inefficien Markes (1s ed.). Oxford: Oxford Universiy Press. 0

21 Table I Descripive saisics for daily TAIEX, TAIFEX and OTC reurns and rading volumes for January 3, 001 o May 7, 009 Panel A: marke reurns Observaions TAIEX TAIFEX OTC Mean Maximum Minimum Sandard deviaion Skewness Kurosis Bera-Jarque [< 0.001] [< 0.001] [< 0.001] Q(8) [0.061] 1.95 [0.07] [< 0.001] Q (8) [< 0.001].30 [< 0.001] [< 0.001] Panel B: rading volume Observaions Mean Maximum Minimum Sandard deviaion Skewness Kurosis Bera-Jarque [< 0.001] [< 0.001] [< 0.001] Q(8) [< 0.001] [< 0.001] [< 0.001] Q (8) [< 0.001] [< 0.001] [< 0.001] Noe. This able provides descripive saisics for daily TAIEX, TAIFEX and OTC reurns and rading volume over he period from January 3, 001 o May 7, 009. Normaliy ess are based on he Bera-Jarque saisics. Q(8) is he Ljung-Box (1978) es for serial correlaion up o he 8 h order in he sandardized residuals, Q (8) is he Ljung-Box es for serial correlaion up o 8 h order in he squared sandardized residuals. Significan a he 5% level is denoed by *. The number in bracke is p-value. 1

22 Table II Regression analysis for he relaionship beween marke reurns and he proxies for senimen indicaor January 3, 001 o May 7, 009 regression model: c + c S + c R + Panel A: scaled volume TAIEX TAIFEX OTC Panel B: deviaed volume TAIEX TAIFEX OTC Panel C: senimen indicaor TAIEX TAIFEX OTC R = ε c 1 c c 3 Q(8) (0.71) (0.38) 0.18 (6.95)** ( ) 0.05 (1.19) (- 1.37) 9.00 [0.31] 1.6 [0.15] < (0.010) 0.1 (5.778)** 0.1 (5.5)** [0.63] c 1 c c 3 Q(8) 0.06 (1.595) (.65)** 0.09 (1.15) (-.318)** (-.987)** (-.51)** (.17)** ( )* (6.890)** 9.3 [0.306] 1.36 [0.07]* [0.118] modified regression model: R = c1 + cs _ H + c3s _ Li, + cri, 1 + ε c 1 c c 3 c Q(8) 0.06 (1.68) 0.10 (.707)** (1.) 0.09 (.13)** (- 6.0)** ( )** (-.31)** (1.85) ( )** 0.09 (1.96) (- 1.65)* (5.936)** 9.8 [0.78] 1.66 [0.066]* [0.0] Noe. This able repors he regression models, expressed by Eq. (1) and (), for TAIEX, TAIFEX and OTC over he whole period from January 3, 001 o May 7, 009. The proxies of senimen indicaor including scaled volume and deviaed volume are considering in regression model. We group deviaed volume ino high and low senimen indicaors and hen incorporae hese senimen indicaors ino modified regression model. Q(8) is he Ljung-Box (1987) es for serial correlaion up o he 8 h order in he sandardized residuals. Significan a he 5% level is denoed by **, a he 10% level by *. The number in brackes is p-value, in parenheses is sandard error.

23 Table III Comparison o GARCH(1,1) model and GARCH(1,1)-mean model, Daily January 3, 001 o May 7, 009 R h = c 1 + c = ω + α ε i S _ H i + β h + c 3 i S _ L 1 + c + θ S _ H h + ε + θ S _ L Simple GARCH:c 1 c c 3 c Panel A: mean equaion wihou lagged condiional volailiy TAIEX 0.078** 0.9** ** (.50) (3.538) (-.59) TAIFEX 0.110** ** ** (3.507) ( ) ( ) OTC 0.069* 0.33** ** (1.881) (3.88) (-.780) ω α β θ 1 θ Q() Q(8) Panel B: condiional volailiy equaion TAIEX 0.010** 0.055** 0.930** 0.101** 0.067** (.336) (7.7) (108.0) (5.513) (.898) [0.15] [0.196] TAIFEX 0.035** 0.075** 0.905** 0.** 0.080* (.7) (8.65) (93.01) (3.909) (1.977) [0.575] [0.558] OTC 0.019** 0.066** 0.93** 0.03** 0.050* 9.068** 51.96** (.53) (8.71) (111.03) (3.57) (1.770) [<0.001] [<0.001] GARCH-in-mean: c 1 c c 3 c Panel C: mean equaion wih lagged condiional volailiy TAIEX TAIFEX OTC (1.351) (1.65) (0.31) 0.63** (3.176) ** (-.7) 0.86** (3.38) ** (- 5.51) ** ( ) ** ( ) (0.36) (1.383) 0.06 (1.057) ω α β θ 1 θ Q() Q(8) Panel D: condiional volailiy equaion TAIEX TAIFEX OTC 0.13** (8.77) 0.16** (8.6) 0.36** (7.575) 0.16** (7.516) 0.138** (8.109) 0.181** (8.35) 0.73** (38.9) 0.769** (8.3) 0.69** (37.579) (1.511) 0.710** (.951) ** (-.95) 0.167** (.19) 0.** (.135) 0.0** (.08) 6.10 [0.171] 5.98 [0.58] ** [<0.001] [0.106] 16.80* [0.039] 55.55** [<0.001] Noe. Panel A and B are he empirical resul for he esimaions of GARCH(1.1) model wih senimen indicaor in condiional volailiy equaion. Panel C and D are he empirical resul for he esimaions of GARCH(1,1)-in-mean model incorporaing senimen indicaor in boh mean and condiional volailiy equaions. Q() and Q(8) are he Ljung-Box (1987) es for serial correlaion up o he h and 8 h order wih he sandardized residuals. In order o simplify he esimaed resuls, our specificaion on mean equaion ignores he auoregressive erm for he ime being. The number in brackes is p-value and in parenheses is sandard error. Significance a he 5% level is denoed by **, a he 10% level by *. 3

24 Table IV Comparison o componen volailiy model in mean on hree specificaions, Daily January 3, 001 o May 7, 009 r q h = c 1 = q + c = ω + ρ q i i + α ( ε i S _ H + c + ϕ ( ε i q 3 S _ L h ) + β ( h i + c q ) + θ S _ H 1 q + c 5 ( h 3 q ) + θ S _ H + θ S _ L ) + ε + θ S _ L Sample period January 3, 001-May Specificaion 1 Specificaion Specificaion 3 Panel A: mean equaion c 1 c c 3 c c 5 Panel B: long erm componen ω ρ ϕ θ 1 θ Panel C: shor-erm componen α β θ 3 θ TAIEX TAIFEX OTC 0.10** ** ** ** ** ** 0.75** ** 0.988** 0.053** 0.113** ** ** 0.169** 1.777** 0.979** 0.083** 0.58** 0.10** ** ** 0.11** 0.586** 0.5** ** ** ** 1.006** 1.710** 0.99** 0.059** 0.05** ** ** 0.73** ** ** 0.16** TAIEX TAIFEX OTC 1.087** 0.99** 0.05** 0.091** ** ** 0.5** ** ** 0.039** **.169** 0.98** 0.08** 0.19** ** ** 1.993** 0.99** 0.058** 0.060** 0.030** 0.758** ** TAIEX TAIFEX OTC ** ** *.8** 0.99** 0.066** Noe. This able repors he esimaions of componen volailiy model on hree specificaions over he whole period from January 3, 001 o May 7, 009. Componen volailiy model including wo aspecs senimen indicaors in volailiy equaions is shown in specificaion 1. Specificaions and 3 are presened ha model conaining jus high and low senimen indicaor individually. Significan a he 5% level is denoed by **, a he 10% level by * ** 0.199** ** ** 0.036* ** 0.993** 0.088** 0.06* ** ** ** ** 0.987** 0.081** **

25 Table V The esimaed resuls of componen volailiy model in mean on hree specificaions before 008 financial sunami r q h = c 1 = q + c = ω + ρ q i + α S _ H i i( ε + c + ϕ ( ε i q 3 S _ L h ) + β ( h i + c q ) + θ S _ H 1 q + c 5 ( h 3 q ) + θ S _ H + θ S _ L ) + ε + θ S _ L Sample period January 3, 001-Augus Specificaion 1 Specificaion Specificaion 3 Panel A: mean equaion c 1 c c 3 c c 5 Panel B: long erm componen ω ρ ϕ θ 1 θ Panel C: shor-erm componen α β θ 3 θ TAIEX TAIFEX OTC 0.100* 0.5** ** ** 0.70** 0.981** 0.051** 0.133** 0.06* ** ** 0.155** 0.099** ** ** ** 0.879** 0.998** ** ** 0.05** 0.07** 0.893** 0.79** 0.053** 0.186** ** 0.365** ** ** ** 0.93** 1.75** 0.989** 0.053** 0.05** TAIEX TAIFEX OTC 1.18** 0.989** 0.060** 0.077** 0.09* ** ** **.016** 0.996** 0.09** ** 0.05** 0.00** 0.00** 0.835** ** 0.903** ** - 0.9** 0.0** 0.136** 0.188** 0.555** ** ** 1.716** 0.99** 0.05** 0.058** 0.030** 0.836** ** TAIEX TAIFEX OTC ** - 0.8** *.101** 0.989** 0.069** < Noe. This able repors he esimaions of componen volailiy model on hree specificaions over he sub-period from January 3, 001 o Augus 31, 008. Componen volailiy model including wo aspecs senimen indicaors in volailiy equaions is shown in specificaion 1. Specificaions and 3 are presened ** 0.191* ha model conaining jus high and low senimen indicaor individually. Significan a he 5% level is denoed by **, a he 10% level by * ** ** 0.01* ** 0.98** 0.097** 0.116* ** * ** ** ** 0.981** 0.08** **

26 High Exreme Senimen Low Exreme Senimen High Exreme Senimen Low Exreme Senimen High Exreme Senimen Low Exreme Senimen TAIEX exreme senimen TAIFEX exreme senimen OTC exreme senimen Figure 1: Exreme senimen indicaors for hree markes This figure plos he exreme senimens separaing ino high (solid line) and low (dashed line) pars for hree markes. These exreme senimens are sored from scaled rading volumes. We sor ou he exreme scaled rading volumes and hen plo he low exreme senimen wih negaive quaniy especially. 6

27 Uncondiional Variance Scaled Trading Volume Uncondiional variance Scaled Trading Volume Uncondiional Variance Scaled Trading Volume TAIEX-Specificaion 1 TAIFEX-Specificaion 1 OTC-Specificaion Uncondiional Variance Scaled Trading Volume Uncondiional Variance Scaled Trading Volume Uncondiional Variance Scaled Trading Volume TAIEX-Specificaion TAIFEX-Specificaion OTC-Specificaion Uncondiional Variance Scaled Trading Volume Uncondiional Variance Scaled Trading Volume Uncondiional Variance Scaled Trading Volume TAIEX-Specificaion 3 TAIFEX-Specificaion 3 OTC-Specificaion 3 Figure : Uncondiional variance and scaled rading volume for hree markes This figure plos he esimaed uncondiional variance (solid line) and scaled rading volume 8 (dashed line) a a daily frequency from January 3, 001 o May 7, 009. The esimaed uncondiional variance is modeled by hree specificaions including considering boh wo aspecs of senimen indicaors in componen volailiy equaions simulaneously (Specificaion 1) and considering single aspec of senimen indicaor respecively (Specificaion and Specificaion 3). Specificaion is described he componen volailiy equaions conaining only high senimen indicaor. However Specificaion 3 is picured he componen volailiy equaions conaining only low senimen indicaor. 8 The scaled rading volume is calculaed by using sandardizaion of he rading volume. 7