Inerdisciplinary Journal of esearch in Business ol. 1, Issue. 7, July 011(pp.81-95) The elaion beween Price Changes and Trading olume: A Sudy in Indian Sock Marke Dr. Naliniprava Tripahy Associae Professor (Finance) Indian Insiue of Managemen Shillong, Meghalaya, India PIN 793 014. E-mail: nalini_prava@yahoo.co.in nalini.607@rediffmail.com ABSTACT This sudy invesigaes he dynamic relaionship beween sock reurn and rading volume of Indian sock Marke by using Bivariae egression model, ECM Model, A, IF and Johansen s Co inegraion es. The sudy shows ha here is a bi-direcional causaliy beween rading volume and sock reurn volailiy. Again he sudy used ariance Decomposiion echnique o compare he degree of explanaory power of he rading volume over sock reurn and he evidence suppors he influenial role of he rading volume in he Indian sock marke. Furher Johansen s co inegraion analysis demonsraes ha sock reurn is co inegraed wih he rading volume indicaing long-run equilibrium relaionship. The sudy concludes ha sock price changes in any direcion have informaion conen for upcoming rading aciviies. Keywords: Sock eurns, Trading olume; Causaliy, Johansen s Co inegraion INTODUCTION The relaionship beween sock reurns and volume has been widely documened in finance lieraure. Karpoff (1987) provides a good review of lieraure and explains ha his relaionship provides insigh ino he srucure of financial markes and is imporan for even sudies for drawing inferences from he use of price and volume in analysis. Numerous papers have documened he fac ha high sock marke volume is associaed wih volaile reurns. I has also been noed ha volume ends o be higher when sock prices are increasing and viceversa. The concep of he volume impac is buil on he fac ha price needs volume o move, hus, he high volailiy of sock prices may be produced as consequence of volume volailiy and rading aciviies. However, since invesors are heerogeneous when inerpreing new informaion, sock reurns may say unchanged even hough new informaion is brough o he marke. On he oher hand, sock reurns may only change if here is posiive rading volume. As i happens wih reurns, rading volume and is changes mainly reflec he available se of relevan informaion perceived by he marke. A large segmen of he finance lieraure invesigaes he link beween informaion and prices. Theory suggess ha prices are funcion of public informaion and order flow (see, for example, Grossman and Sigliz (1980) and Glosen and Milgrom (1985)).Order flow is driven by boh public and privae informaion as well as invesor shocks, which may be eiher raional (e.g., no informaion-based liquidiy rades) or irraional (e.g., rades based on noise as described by Black (1976)). Prices can deviae from fundamenal value due o marke microsrucure, liquidiy, and hedging effecs. Pricing errors can arise from noise rading and due o under reacion or overreacion o informaion. So, in his conex, deeper undersanding of he role of rading volume and relaionship wih sock reurn may help invesors o idenify fuure paerns of he sock marke which can be used in heir invesmen decisions. Secondly Sock price-volume relaion can also be used as basis of rading sraegy for efficiency of sock markes. Thirdly, he relaionship beween sock price and volume can be used o examine he usefulness of echnical analysis. However, here is lile sudy is made in India during Asian crisis and world sock marke crisis period 005-010.This moivae us for exploring research in Indian Sock Marke o deermine he role of rading volume and volailiy in he dynamics of price discovery process in India. So, in his paper, we have raised hree research quesion.firs his paper will add o he exising lieraure by providing robus resul. Secondly we invesigae he causal relaionships beween reurn volailiy and rading volume in Indian sock marke. We also use ecor Auo regression (A) model o examine he shor run causaliy beween sock price and volume. Thirdly, we have used Johansen s Co inegraion es o deermined he long-run relaionship beween sock reurn and volume in India o obain new insighs. Therefore, he presen work improves he earlier sudies and offers a value addiion o he exising lieraure and proves o be useful o he invesors as well as regulaors. The res of he paper proceeds as follows: In secion wo we provide a brief review of pas lieraure relaing o he causal relaionship beween sock reurns and rading volume. Secion hree describes he daa & mehodology 81
Inerdisciplinary Journal of esearch in Business ol. 1, Issue. 7, July 011(pp.81-95) used in he sudy. Secion four discusses he empirical findings while he las secion offers some concluding observaion. LITEATUE EIEW The discussion in lieraure on price and volume relaionship has been approached from various perspecives, which include he relaion beween price changes and volume (Epps and Epps, 1976), absolue price changes and volume (Clark, 1973; Wood e al. 1985), causal relaionship beween price and volume (Wang, 1994; Ciner, 00), and rading volume and condiional volailiy (Lamoureux and Lasrapes, 1994). These sudies demonsraed ha rading volume is posiively relaed o sock prices. Hiemsra and Jones (1994) used nonlinear Granger causaliy ess o examine he nonlinear causal relaion beween percenage changes in he NYSE rading volume and daily Dow Jones Sock eurns and found ha here is a posiive nonlinear bidirecional relaionship beween reurns and volume. Bhaga and Bhaia (1996) also employed daily daa o es he causal relaionship beween volume and reurn, finding reurn causes volume bu no vice versa. Basci e al (1996) used weekly daa on 9 individual socks in Turkey and found he price level and volume is co inegraed. Saacioglu and Sarks (1998) used monhly daa from six Lain American sock markes o es he relaion beween price changes and volume, found a posiive price-volume relaion and a causal relaionship from volume o sock price changes bu no vice versa. Chordia and Swanminahan (000) found ha pas rading volume can be used o predic fuure sock price momenum. aner and Leal (001) examined he Lain American and Asian financial markes and found a posiive conemporaneous relaion beween reurn and volume in hese counries excep India. A he same ime hey observed ha here exiss a bi-direcional causal relaion beween reurn and volume. In summary, he reurn and volume are srongly relaed conemporaneously bu here is lile evidence ha eiher can be used o predic he oher. De Medeiros and Doornik (006) invesigaed he empirical relaionship beween sock reurns, reurn volailiy and rading volume in Brazilian sock marke and found he suppor for a conemporaneous as well as dynamic relaionship beween sock reurns and rading volume. Zolonoy and Melenberg (007) sudied he dynamic relaionship beween rading volume, volailiy, and sock reurns a he inernaional sock markes and heir findings suggesed he imporance of he rading volume as an informaion variable. Sabri (008) found ha he volume-sock price movemens are significanly inegraed for all seleced markes. TIME SEIES DATA &METHODOLOGY Bombay Sock Exchange is he oldes sock exchange in Asia and oday, i is he world's 5h mos acive in erms of number of ransacions handled hrough is elecronic rading sysem. I is also in he op en of global exchanges in erms of he marke capializaion of is lised companies.bse have faciliaed he growh of he Indian corporae secor by providing wih an efficien capial raising plaform. The BSE Index, SENSEX, is India's firs and mos popular Sock Marke Benchmark Index. So we have aken BSE sensex for our sudy. Similarly rading volume refers o he number of shares raded during a defined ime period. When invesors or financial analyss see a large increase in volume, i may indicae a significan change in he price of securiy. Significan volume spikes may indicae some kind of imporan news aking place in he sock marke. We have aken rading volume as anoher variable o deermine is impac on sock marke as well. The required ime series daa is based on daily closing price of BSE SENSEX, acively raded 30 scrips and Trading volume have been colleced from Bombay Sock Exchange for a period of five years from January 005 o January 010. We have chosen he daa period 005 o 010 because during his period Indian sock markes have undergone subsanial policy changes characerized by he revival of privae foreign capial flows o emerging marke economies, flexible exchange raes, srong economic growh, credi marke crisis in he Unied Saes and sharp fell in Asian marke. These changes have affeced he movemen of index and magniude of volume rades in he marke in differen ways. eurns are proxied by he log difference change in he price index. The sock reurn is calculaed as he coninuously-compounded reurn using he closing price: Where ln (P ) denoes he naural logarihm of he closing price a ime Prior o modeling any relaionship, non-saionariy mus be esed. Saionariy means ha he mean and variance of he series are consan hrough ime and he auo covariance of he series is no ime varying (Enders, 004). For applicaion of granger causaliy es, A model and Impulse esponse Funcion, he iniial sep in he esimaion involves he deerminaion of he imes series propery of each variable individually by 8
Inerdisciplinary Journal of esearch in Business ol. 1, Issue. 7, July 011(pp.81-95) conducing uni roo ess. For he purpose of his sudy, we use he model proposed by Augmened Dickey Fuller (ADF) es, Phillip-Perron (PP) es and he Kwiakowski, Phillips, Schmid and Shin (KPSS) es. ADF (Augmened Dickey-Fuller) Tes The uni roo es is carried ou by using he Augmened Dickey Fuller (ADF) es. The mahemaical expression of he ADF es for rend is y (1 ) Y 1 n 1 Y p Y is he variable esed for uni roo and Δ is he firs difference operaor; β is he consan erm; T is he ime rend and n is he lag number. If he series is saionary hen (1- β) =1, and agains his, if model deec non saionariy in daa series hen (1- β) < 1. So he hypoheses of our sudy are: H 0 Time series is saionary and H 1 Time series is nonsaionary. The null hypohesis of he sudy is rejeced if he saisical value is lesser han he criical value and daa series will be considered as non saionary (following he random walk). This implies ha Y is non-saionary and does no conain uni roo. PP Tes To make up for he shorcomings of he ADF es we used he Phillips-Perron es, which allows he error disurbances o be weakly dependen and heerogeneously disribued.furher uni roo es is carried ou using he Phillip-Perron (PP) es and he Kwiakowski, Phillips, Schmid and Shin (KPSS) es, so as o validae he resul of ADF es. The mahemaical expression of he PP es is; y Y 1 X Where Y is he sock price index esed for uni roo. X are opional exogenous regressors ha could eiher be rended or none rended. are he parameers o be esimaed and are he error erms. The null and alernaive hypohesis of his es is Ho: α =0 and H 1 = α > 0 The null hypohesis ha he sock price index does no conain uni roo is acceped when he es saisic is less han he criical value a he seleced level of significance. KPSS (Kwiakowski, Phillips, Schmid, and Shin) Tes In he KPSS es, saionariy is he null hypohesis and he exisence of a uni roo is he alernaive. KPSS ess are used for esing a null hypohesis ha an observable ime series is saionary around a deerminisic rend. The series is expressed as he sum of deerminisic rend, random walk, and saionary error, and he es is he LM es of he hypohesis ha he random walk has zero variance. KPSS ype ess are inended o complemen uni roo ess, such as he ADF ess. The KPSS ess is shown by he following equaion 1 y x The LM saisics is given by: T LM s / 1 Where, is an esimaor for he error variance. This laer esimaor may involve correcions for auocorrelaion based on he Newey-Wes formula. In he KPSS es, if he null of saionariy canno be rejeced, he series migh be co inegraed. The KPSS es is esimaed and found o conain a uni roo when he es saisics is less han he criical values a he esimaed level of significance. Sock eurns and Trading olume To es he conemporaneous relaionship beween sock reurns and rading volume, we apply he mulivariae model proposed by (Lee; ui, 00): 83
Inerdisciplinary Journal of esearch in Business ol. 1, Issue. 7, July 011(pp.81-95) o o 1 1 1 3 1 1 1 1 3 1 u v and are sock reurn and rading volume respecively.α 1 andβ 1 are model parameer and u and v are whie noise error erm. I is ofen repored ha price flucuaions end o increase if here is high rading volume, especially in imes of bullish markes. I may happen due o relaion beween higher orders momens of sock reurns and rading volume. We scruinize his by exending a model which relaes rading volume o squared sock reurns by he following regression (Brailsford. 1996): o 1 1 1 D Where D is a dummy variable ha equals o 1 if he is posiive and 0 if negaive. The esimaed parameer α 1 measures he relaionship beween reurn volailiy and rading volume irrespecive of he direcion of price change. The esimaed parameer α measures he degree of asymmery in ha relaionship. Co inegraion Tes Engle and Granger (1987) poined ou ha a linear combinaion of wo or more non-saionary series may be saionary. If such a saionary linear combinaion exiss, he non-saionary ime series are said o be co inegraed. The purpose of he co inegraion es is o deermine wheher a group of non-saionary series is co inegraed or no. The presence of a co inegraing relaion forms he basis of he EC specificaion. Afer idenifying he order of inegraion, we hen use he Johansen s (1991, 1995a) co inegraion es o deermine wheher here is a long-run relaionships beween he various series. The Johansen s echnique for esimaing co inegraion is superior because i is based on well-esablished maximum likelihood procedure ha provides es saisics o deermine number of co inegraion vecors as well as heir esimaes. The exisence of more han one co inegraing vecor implies higher sabiliy in he sysem. The co inegraion esing procedure suggesed by Johansen s (1991, 1995a) o es he resricions imposed by co inegraion on he unresriced A involving he series. Considering a A of order : e Y A Y 1 1 ApY p BX Where Y is a K-vecor of non-saionary 1(1) variable, X is a d vecor of deerminisic variables and ε is a vecor of innovaions. I can rewrie he A as Y Y p1 T Y 1 i 1 i1 BX Where p i1 A, 1 T i p ji1 Aj Granger s represenaions heorem assers ha if he coefficien marix has reduced rank r<k, hen here exis Kr marixes and & β each wih rank r such ha =β is saionary r is he number of co inegraing relaions and each column of is he co inegraing recor. The elemens of are known as he adjusmen parameers in he vecor error he marix in an unresriced form. The Johansen approach o co inegraion es is based on wo es saisics, viz., he race es saisic, and he maximum Eigen value es saisic. Trace Tes Saisic The race es saisic can be specified as: race k T log(1 i ), where i is he ih larges Eigen value of marix and T is he number of observaions. In he race es, he null hypohesis is ha he number of disinc co inegraing vecor(s) is less han or equal o he number of co inegraion relaions ( r ). ir1 84
Inerdisciplinary Journal of esearch in Business ol. 1, Issue. 7, July 011(pp.81-95) Maximum Eigen value Tes The maximum Eigen value es examines he null hypohesis of exacly alernaive of r 1co inegraing relaions wih he es saisic: r co inegraing relaions agains he max T r 1 ( r 1) h larges squared Eigen value. In he race es, he null hypohesis of alernaive of r 1 co inegraing vecors. log(1 ), r 0 where r 1 is he is esed agains he I is well known ha Johansen s co inegraion es is very sensiive o he choice of lag lengh. So firs a A model is fied o he ime series daa in order o find an appropriae lag srucure. The Akaie Informaion Crierion (AIC), Schwarz Crierion (SC) and he Likelihood aio (L) es are used o selec he number of lags required in he co inegraion es. ecor Error Correcion Model Once he co inegraion is exis beween variables hen he nex sep requires he consrucion of error correcion mechanism o model dynamic relaionship. The purpose of he error correcion model is o indicae he speed of adjusmen from he shor-run equilibrium o he long-run equilibrium. A vecor error correcion (EC) model is a resriced A designed for use wih nonsaionary series ha are known o be coinegraed. The EC has coinegraion relaions buil ino he specificaion so ha i resrics he long-run behaviour of he endogenous variables o converge o heir coinegraing relaionships while allowing for shor-run adjusmen dynamics. The coinegraion erm is known as he error correcion erm since he deviaion from long-run equilibrium is correced gradually hrough a series of parial shor-run adjusmens. Co inegraion implies ha he ransiory componens of he series can be given a dynamic error correcion represenaion; one ha allows for flexibiliy in he shor-run dynamics bu consrains he model o reurn o long-run equilibrium (see Engle and Granger, 1987). If here is evidence of a co inegraing relaionship, causal inferences can be made by esimaing he parameers of he following vecor error correcion model (ECM) equaion. The ECM model allows us o differeniae beween he shor- and long-run dynamic relaionships, and ess for he hypohesis ha he coefficiens of lagged variables and he error correcion erms calculaed from he co inegraing regression are zero. If he coefficiens in he sysem are joinly significan, hen he lagged variables in he sysem are imporan in predicing curren movemens of he dependen variables (i.e., he shor run dynamics), and he dependen variables in he equaion adjus o he previous period s equilibrium error.in his paper he error correcion model as suggesed by Hendry has been used. The general form of he ECM is as follows: m n X 0 1EC 1 ix i jy j 1 i1 j1 m n Y 0 EC 1 ix i jx j i1 j1 Where is he firs difference operaor; EC 1 1 0 is he error correcion erm lagged one period; is he shorrun coefficien of he error correcion erm ( ); and is he whie noise. The error correcion coefficien ( ) is very imporan in his error correcion esimaion as greaer he co-efficien indicaes higher speed of adjusmen of he model from he shor-run o he long-run. The error correcion erm represens he long-run relaionship. A negaive and significan coefficien of he error correcion erm indicaes he presence of long-run causal relaionship. If he boh he coefficiens of error correcion erms in boh he equaions are significan; his will sugges he bi-direcional causaliy. If only negaive and significan, his will sugges a unidirecional causaliy from Y o X. Similarly, if 1 is is negaive and significan, his will sugges a unidirecional causaliy from X o Y. On he oher hand, he lagged erms of and appeared as explanaory variables, indicae shor-run cause and effec relaionship beween he wo X 85
Inerdisciplinary Journal of esearch in Business ol. 1, Issue. 7, July 011(pp.81-95) variables. Thus, if he lagged coefficiens of X mean ha X causes Y. Similarly, if he lagged coefficiens of of X, his will mean ha Y causes X. appear o be significan in he regression of, his will appear o be significan in he regression To examine he conemporaneous relaion beween sock reurns and rading volume, we have used Granger Causaliy Tes. The Granger Causaliy es is used o invesigae wheher he pas informaion of volailiy is useful o improve he predicion of rading volume and vice versa. We es wheher rading volume causes reurn or reurn causes rading volume by employing bivariae A model. This sudy relies on he convenional F-es for join exclusion resricions. ariance Decomposiion (DC) and Impulse esponse Funcion (IF) The A by Sims (1980) has been esimaed o capure shor run causaliy beween sock reurn and rading volume. arious decomposiion and impulse response funcion has been uilized for drawing inferences. The DC is an esimae of he proporion of he movemen of he n-sep ahead forecas error variance of a variable in he A sysem ha is aribuable o is own shock and ha of anoher variable in he sysem. Similarly, he IF shows impulse responses of a variable in he A sysem o he ime pah of is own shock as well as ha of he shock o anoher variable in he sysem. While impulse response funcions race he effecs of a shock o one endogenous variable o he oher variables in he A, variance decomposiion separaes he variaion in an endogenous variable ino he componen shocks o he A. Thus, he variance decomposiion provides informaion abou he relaive imporance of each random innovaion in affecing he variables in he A. EMPIICAL FINDINGS Uni oo Tess: The sudy here employs he uni roo es o examine he ime series properies of concerned variables. For he es of uni roo he presen sudy employees he Augmened Dickey Fuller es, PP es and KPSS es. The able 1 repors ha he value of ADF es of all variable is less han is criical values a 1%, 5% and 10% respecively. Therefore he sudy rejecs he null hypohesis and concludes ha daa series is non-saionary and following he random walk. The saisical values of DF-GLS, PP and KPSS are also lesser han heir corresponding criical values and rejecing he null hypohesis of saionariy. Descripive Saisics Table 1 goes here The basic descripive analysis of he ime series of sock reurns and rading volume is shown in Figures 1,and able. All reurns are calculaed as he firs difference of he log of he daily closing price. Daily rading volume and sock reurn have posiive kurosis and high JB saisics ha implies ha he disribuion is skewed o he righ and hey are lepokuric((heavily ailed and sharp peaked), i.e., he frequency disribuion assigns a higher probabiliy o reurns around zero as well as very high posiive and negaive reurns. The Jarque Bera saisic es indicaes ha he null hypohesis of normaliy is rejeced and shows ha all he series exhibi nonnormaliy. Squared value of daily sock reurn is used o proxy reurn volailiy. Figure 1 goes here Figure goes here Table goes here Conemporaneous relaionship beween sock reurns and rading volume Table 3 indicaes he Conemporaneous relaionship beween sock reurns and rading volume. The parameer 3 is significan a he 1% level and i is posiive. There is no evidence of lagged relaionship beween sock reurns and rading volume, since he parameer is posiive bu insignifican. However, he conemporaneous relaionship beween sock reurns and rading volume is no simulaneous, since he parameer 1 is no 86
Inerdisciplinary Journal of esearch in Business ol. 1, Issue. 7, July 011(pp.81-95) significan, which means ha depends on, bu does no depend on. The srong ime dependency of rading volume is documened by highly significan parameers able 4. Table 3 goes here Table 4 goes here and 3 which is depiced in he following Again we examined he relaion beween higher order momens of sock reurns volailiy and rading volume. So we exend he model which relaes rading volume o squared sock reurn (Brails ford, 1996).Table-5 1 is posiive and highly significan a 1%level indicaing relaionship beween reurn repored ha parameer volailiy and rading volume irrespecive of he direcion of he price changes. I suggess ha higher rading volume is associaed wih an increase in sock reurn volailiy. The parameer is insignifican suggesing ha here is asymmery relaionship beween reurn volailiy and rading volume. The analysis poins ou ha news is having impac on rading volume. So, good news increasing he sock reurn volailiy lead o increase rading volume and bad news decrease he sock reurn volailiy and reducing he rading volume. Table 5 goes here Table 6 goes here Table 6-a goes here Table 6-b goes here The able 6 indicaed one co inegraing vecor a 5% level of significance. So i rejecs he null of no co inegraion a he convenional level of significance and indicaes ha sock reurn is co inegraed wih he rading volume and has a long-run equilibrium relaionship wih i. However, i is possible ha co inegraing variables may deviae from heir relaionship in he shor run, bu heir associaion would reurn in he long run. Table 7 goes here Using a ECM for he period January 005 hrough January 010, he esimaed resuls Shown in Table 7, sugges ha he long-run elasiciy of he Indian sock marke o he rading volume is almos 16.81. In oher words, a one percen deviaion in he rading volume decreases he sock reurn by 16.81 percen. The negaive saisically significan value of error correcion coefficien indicaes he exisence of a long-run causaliy beween he sock reurn volailiy and rading volume of he sudy. Table 8 goes here The able 8 exhibis ha here is bi-direcional causaliy beween rading volume and sock reurn volailiy. This specifies ha sock price changes in any direcion have informaion conen for upcoming rading aciviies. There is no evidence of causaliy beween sock reurns and rading volume in eiher direcion. I is eviden from he analysis ha influence of lagged sock reurns on rading volume is insignifican. Table 9 goes here The able-9 shows he resuls for he DC analysis. The variance decomposiion echnique for a period of 10 monhs ahead indicaes ha he Indian sock marke is affeced by rading volume. The variabiliy of rading volume is explained by he shocks o sock reurn is 99% a 10 lags. The role of sock reurns increase from 0.6% in he beginning of he period o 7.6% a he end of he period. In sum, he evidence suppors he influenial role of he rading volume on he Indian sock marke. The resuls provide srong evidence in suppor of he argumen ha he movemens of sock reurns are explained by heir own shocks raher han he shocks o he rading volume. The variabiliy of sock reurn is explained by he shocks o rading volume is 99% a 10 lags. The role of rading volume increases from 7% in he beginning of he period o 10% a he end of he period. The variance decomposiion analysis provides he evidence of pas shock reurns in predicing fuure rading volume. Table 10 goes here Figure 3 goes here 87
Inerdisciplinary Journal of esearch in Business ol. 1, Issue. 7, July 011(pp.81-95) To furher invesigae he dynamic responses beween he rading volume and sock reurn, he impulse response of he A sysem has been calculaed and exhibied in he able-10and fig-3. I is observed from he able 10 ha a one sandard error shock in sock reurn affecs rading volume negaively ill around 10 monhs while one sandard- error shock in rading volume affecs sock marke posiively ill around 10 monhs. Boh impulse responses fall beween he respecive sandard error bands. We find evidence of disinc asymmery in he impulse responses beween sock reurns and rading volume. Shocks o rading volume do no end o have significan impac on heir corresponding reurns. In sock markes, shocks o reurns are imporan in predicing he fuure dynamics of heir own reurn series and he fuure dynamics of heir corresponding rading volume values. So he sudy revealed ha shocks in sock reurns impac rading volume in he expeced direcion over a shor horizon. CONCLUDING OBSEATION This sudy invesigaes he relaionship beween rading volume and sock reurns using he daa during January 005 o January 010. We found he evidence of significan conemporaneous relaionship beween reurn volailiy and rading volume and indicae ha informaion may flow simulaneously raher han sequenially ino he marke. Apar of i he sudy also found ha rading volume is associaed wih an increase in reurn volailiy and his relaionship is asymmerical. This implied ha daily new informaion in marke may have significan impac on price volailiy. So he sudy indicaes ha bad news generae more impac on volailiy of he sock reurn and rading volume. One explanaion may be ha normally invesors have a higher aversion o downside risk, so hey reac faser o bad news. Addiionally variance decomposiion and impulse response funcion are also esimaed o undersand he dynamic relaionship beween sock reurn and rading volume. The sudy revealed ha shocks in sock reurns impac rading volume in he expeced direcion over a shor horizon. Bu Co inegraion analysis shows ha sock reurn volailiy is co inegraed wih he rading volume indicaing long-run equilibrium relaionship. The error correcion model also indicaes he exisence of a longrun causaliy beween he sock reurn volailiy and rading volume of he sudy. I is eviden ha ha volailiy moves in sympahy wih rading aciviy in he primary marke. Since exisence of excessive volailiy, or noise, undermines he usefulness of sock prices as a signal abou he rue inrinsic value of a firm, Invesors, analyss, brokers, dealers and regulaors are more concerned abou sock reurn volailiy. So he pas informaion of rading volume is useful o improve he predicion of sock price volailiy suggess ha regulaors and raders can use pas informaion for monioring volailiy level in he marke. So i suggess ha he auhoriies can focus more on domesic economic policies o sabilize he sock marke. One of he limiaions of he sudy is ha we have employed he radiional Granger Causaliy es. Since i is now recognized ha he convenional procedure may be inadequae, conclusions based on such an approach may yield misleading inferences. However he findings of he sudy are subjec o he period of he sudy seleced and he resul may change if he sudy period will change. EFEENCES 1. Al Janab, Mazin A.M. (007), Equiy rading risk managemen: he case of Casablanca Sock Exchange, Inernaional Journal of isk Assessmen and Managemen, 7, (4); 535-568.. Al-Khouri iab S. and Moh d M. Ajlouni (007), Narrow Price Limi and Sock Price olailiy: Empirical Evidence from Amman Sock Exchange" Inernaional esearch Journal of Finance and Economics, 8; 163 180. 3. Black, F., (1976), Sudies of sock price volailiy changes, Proceedings of he 1976 meeings of he American Saisical Associaion, Business and Economics Saisics Secion, Washingon, DC: American Saisical Associaion, 177-181. 4. Bollerslev, T. (1986), Generalized auoregressive condiional heeroscedasiciy, Journal of Economerics, 307-37. 5. Basci, Erdem, Suheyla Ozyildinm and Kursa Aydogan (1996), "A noe on price-volume dynamics in an emerging sock marke" Journal of Banking & Finance 0; 389-400. 6. Bhaga, S., Bhaia, S., (1996), Trading olume and Price ariabiliy: Evidence on Lead-lag elaions from Granger-Causaliy Tess, Working Paper, Universiy of Colorado a Boulder. 7. Brailsford, T.J. (1996), The empirical relaionship beween rading volume, reurns and volailiy, Accouning and Finance 35 (1): 89-111. 8. Ciner, C., (00), Informaion conen of volume: an invesigaion of Tokyo commodiy fuures markes, Pacific-Basin Finance Journal, 10, 01-15 9. Clark, P., (1973), Subordinaed sochasic process model wih finie variance for speculaive prices, Economeric a, 41, 135-155. 88
Inerdisciplinary Journal of esearch in Business ol. 1, Issue. 7, July 011(pp.81-95) 10. Copeland, T., (1976), A model of asse rading under he assumpion of sequenial informaion arrival, Journal of Finance, 31, 1149-1168 11. Chordia, Tarun & Swaminahan, B., (000) Trading volume and Cross-Auocorrelaions in Sock eurns, Available a SSN: hp://ssrn.com/absrac=157835 1. Daar, M. K. and Basu, P. K., (004), Financial secor reforms in India: Some insiuional imbalances, Conference olume, Academy of World Business, Markeing and Managemen Developmen Conference, Gold Coas, Ausralia, July. 13. De Medeiros, Oavio. and an Doornik, Bernardus F. N., (006), The Empirical elaionship beween Sock eurns, eurn olailiy and Trading olume in he Brazilian Sock Marke Available a SSN: hp://ssrn.com/absrac=897340 14. De Mark, T.., (1994), The New Science of Technical Analysis, John, Wiley and Sons, Inc, New York. 15. Dickey, D. A. & Fuller, W.A. (1979) Disribuion of Esimaors for Auoregressive Time Series wih a Uni oo, Journal of he American Saisical Associaion, 74(366), 47-431. 16. Dickey, D. A. & Fuller, W.A. (1981), Likelihood aio Saisics for Auoregressive Time Series wih a Uni oo, Economerica, 49(4), 1057-107. 17. Epps, T., and M. Epps, (1976), The Sochasic Dependence of Securiy Price Changes and Transacion olumes: Implicaions for he Mixure-of-Disribuions Hypohesis, Economeric a, 44,305-31. 18. Enders, Waler. (004), Applied Economeric Time Series, Second Ediion, John Wiley & Sons, Inc., iver Sree, Hoboken, New Jersey. 19. Engle,.F., & Granger, C.W.J, (1987), Co-inegraion & error correcion: represenaion, esimaion &Tesing Economerica, 55,51-76 0. Glosen, L..,. Jagannahan, and D.E. unkle, (1993), On he relaion beween he expeced value and he volailiy of he nominal excess reurns on socks, Journal of Finance, 48, 1779-1801 1. Glosen, Lawrence., and Paul. Milgrom, (1985), Bid, ask and ransacion prices in a specialis marke wih heerogeneously informed raders, Journal of Financial Economics 14, 71 100. Grossman, Sanford J., and Joseph E. Sigliz, (1980), On he impossibiliy of informaion ally efficien markes, American Economic eview 70, 393 408 3. Hiemsra, C. and J. D. Jones, (1994), Tesing for linear and nonlinear Granger causaliy in he sock price-volume relaion, Journal of Finance, 49, 1639-1665. 4. Harrod,. (1939) An Essay in Dynamic Theory, Economic Journal, 49,14-33. 5. Johansen, S., (1991), Esimaion and Hypohesis Tesing of Co inegraion ecors in Gaussian ecor Auoregressive Models, Economerica 58, 165-188 6. Johansen, S. (1995a), Likelihood Based inference in co inegraed vecor Auoregressive Models Oxford: Oxford universiy press. 7. Karpoff, J., (1987), The relaion beween price changes and rading volume: A survey, Journal of Financial and Quaniaive Analysis,, 109-16. 8. Kwiakowski, D., Phillips, P., Schmid, P., & Shin, Y. (199), Tesing he null hypohesis of Saionariy agains he alernaive of a uni roo, Journal of Economerics, 54 (1-3), 159-178. 9. Lakonishok, J., and S. Smid, (1989), Pas price changes and curren rading volume, The Journal of Porfolio Managemen, 15, 18-4 30. Lamoureux, C. G. and Lasrapes, W. D. (1994), Endogenous rading volume and momenum in sockreurn volailiy, Journal of Business and Economic Saisics, 1(): 53-60. 31. Lee B-S, ui, O.M. (00), The dynamic relaionship beween sock reurns and rading volume: Domesic and cross-counry evidence, Journal of Banking and Finance 6 (1): 51-78. 3. Murphy, J.J., (1985), Technical Analysis of he Fuures Marke, Englewood Cliffs: Prenice 33. Hall 34. Mei, Jianping, José A. Scheinkman, and Wei, Xiong, (005), "Speculaive Trading and Sock Prices: Evidence from Chinese A-B Share Premia", AFA 005 Philadelphia Meeings. Available a SSN: hp://ssrn.com/absrac=49804 35. Phillips, P. C. B., & Perron, P. (1988), Tesing for a uni roo in ime series regression, Biomerika. 75, 335-346 36. aner. M. and.p.c. Leal, (001), Sock eurns and Trading olume: Evidence from he Emerging Markes of Lain America and Asia, Journal of Emerging Markes 6(1), 5-. 37. oll, ichard, (1984), A Simple Implici Measure of he Effecive Bid-Ask Spread in an Efficien 38. Marke, Journal of Finance 39, 117-1139. 39. Saaccioglu, Kemal and Laura T. Sarks, (1998), The Sock price- volume relaionship in emerging sock markes: The case of Lain America Inernaional Journal of Forecasing 14; 15-5. 89
Inerdisciplinary Journal of esearch in Business ol. 1, Issue. 7, July 011(pp.81-95) 40. Silvapulle, P. and J. S. Choi, (1999), Tesing for linear and nonlinear Granger causaliy in he sock price-volume relaion: Korean evidence, Quarerly eview of Economics and Finance 39, 59-76. 41. Sabri, Nidal ashid,(008), The Impac of Trading olume on Sock Price olailiy in he Arab Economy. Available a SSN: hp://ssrn.com/absrac=109764 4. Sims, C. A. (1980), Macroeconomics and ealiy, Economerica, 48(1): 1-48. 43. Wang, J. (1994), A model of compeiive sock rading volume. Journal of Poliical Economy 44. 10: 17-168 45. Tripahy, Naliniprava. (010) The Empirical elaionship beween Trading olumes & Sock eurn olailiy in Indian Sock Marke European Journal of Economics, Finance and Adminisraive Sciences, 1450-75 Issue 4. 46. Wood,., T. Mdnish, and J. Ord. (1985), An invesigaion of ransacions daa for NYSE socks,. Journal of Finance 60: 73-739. 47. Zolooy Leon & Melenberg Berrand,(007) Trading olume, olailiy and eurn Dynamics: Individual and Cross-Marke Analysis, Available a SSN: hp://ssrn.com/absrac=103193 Table(s) and Figure(s) Table 1: Augmened Dickey-Fuller, PP Tes & KPSS Uni oo Tes variable ADF Tes DF-GLS Tes PP Tes KPSS (LM sa) -11.30398-4.7975-31.06757 0.184359-0.160904-0.444803-11.6719 0.401354-6.04618-4.389949-35.86743 0.049851 Noe: ADF criical values wih an inercep and no rend are: -3.436, -.864 and -.568 a 1%, 5% and 10% levels; PP criical values are: -3.436, -.864 and -.568 a 1%, 5% and 10% respecively. KPSS criical values are: 0.739, 0.463, and 0.347 a 1%, 5%, and 10% levels, DF-GLS criical values are -.567,-1.941,-1.617 a 1%, 5% and 10% levels. Null of saionariy is acceped if he ess saisic is less han he criical value. Figure 1: Daily Sock eurns (005-010).0.15.10.05.00 -.05 -.10 -.15 50 500 750 1000 Sock eurn 90
Inerdisciplinary Journal of esearch in Business ol. 1, Issue. 7, July 011(pp.81-95) Figure : Daily olume Change (005-010) 1 8 4 0-4 50 500 750 1000 Daily olume o Table- Descripive Saisics Mean 0.1853 0.00077 110.4779 0.11353 Median 0.000000 0.001536 0.000703 0.000000 Maximum 11.3585 0.159900 15937.65 11.3585 Minimum -3.778490-0.116044 0.000000-3.778490 Sd. Dev. 1.1953 0.019886 1156.70 1.155778 Skewness 7.564556 0.108384 10.59874 7.780010 Kurosis 66.04969 8.979364 115.817 70.4336 Jarque-Bera 19444.1 1655.740 603863.9 1507.9 Probabiliy 0.000000 0.000000 0.000000 0.000000 Table-3 Conemporaneous relaionship beween sock reurns and rading volume 1 1 1 3 1 u Coefficien Sd. Error -Saisic Prob. α o 0.000695 0.000599 1.1600 0.46 α 1-0.000404 0.000819-0.49404 0.614 α 0.000169 0.000847 0.199878 0.8416 α 3 0.077793 0.09984.594495 0.0096* *Significan a 1% 1 Diagnosic Saisics Adjused -squared 0.003699 Log likelihood 784.386 Durbin-Wason sa 1.994615 Akaike info crierion -4.99601 Schwarz crierion -4.978179 F-saisic.3760 Prob(F-saisic) 0.06850*** 91
Inerdisciplinary Journal of esearch in Business ol. 1, Issue. 7, July 011(pp.81-95) o Table-4 Conemporaneous relaionship beween sock reurns and rading volume 1 1 1 3 1 v Coefficien Sd. Error -Saisic Prob. β o 0.00743 0.018993 1.09140 0.750 β 1-0.849749 0.950411-0.894086 0.3715 β 0.4569 0.0601 16.36510 0.0000* β 3 0.55985 0.07010 19.47406 0.0000* *Significan a 1% Diagnosic Saisics Adjused -squared 0.758 Log likelihood -1059.916 Durbin-Wason sa.16013 Akaike info crierion 1.91540 Schwarz crierion 1.93389 F-saisic 964.4680 Prob(F-saisic) 0.000000 Table-5 Conemporaneous relaionship beween squared sock reurns volailiy and rading volume o 1 1 1 1 D v Coefficien Sd. Error -Saisic Prob. α o 0.010097 0.018748 0.538533 0.5903 Ø 1 0.16950 0.037883 4.30146 0.0000* Ø 0.151786 0.048090 3.156306 0.0016* α 1 0.06430 0.006944 9.59897 0.0000* α -788.0575 000.86-0.393859 0.6938 *Significan a 1% Diagnosic Saisics Adjused -squared 0.7410 Log likelihood -1018.775 Durbin-Wason sa.09977 Akaike info crierion 1.84980 Schwarz crierion 1.865541 F-saisic 799.5074 Prob(F-saisic) 0.000000 Table-6 Johansen s Co inegraion es Assumpions: No deerminisic rend in he series in levels and no inercep in he co inegraing equaion ariable Eigenvalue Trace Saisic Hypohesized No. Of CE (S) 0.05 Criical alue (p-value) Maximum Eigen saisics 0.05 Criical alue (p-value) Sock reurn volailiy 0.56893 39.738* 1.3090 ((0.0001) 38.3879 11.480(0.0001) None * Trading volume 0.000801 0.885877 4.19906 ( 0.4008) 0.885877 4.19906( 0.4008) A mos 1 Trace es indicaes 1 co inegraing eqn(s) a he 0.05 level * denoes rejecion of he hypohesis a he 0.05 level 9
Inerdisciplinary Journal of esearch in Business ol. 1, Issue. 7, July 011(pp.81-95) Table -6a Unresriced Adjusmen Coefficiens (alpha) Sock reurn volailiy 0.011473-1.91E-05 Trading volume 0.003836 0.017594 Log Likelihood 1740.37 Table- 6b Normalized co inegraing coefficiens (sandard error in parenhesis) Sock reurn volailiy Trading Log Likelihood volume 1.0000 6.9E-05 (0.00059) 1740.37 Table-7 ecor Error Correcion Esimaes Lis of ariables CoinEq1 Trading volume 1.000000 Sock reurn volailiy 65747.55 (-saisics) (3911.1) (P-value) [ 16.8100] C -5.9506 Lis of ariables Trading volume Sock reurn volailiy EC -1 (-saisics) (P-value) 0.00018 (0.00040) [ 0.31999] -1.11E-05 (6.6E-07) [-16.8056] Trading volume -1 (-saisics) (P-value) Trading volume - (-saisics) (P-value) Sock reurn volailiy -1 (-saisics) (P-value) -0.649736 (0.0970) [-1.8763] -0.173131 (0.097) [-5.8543] 5.359149 (.9197) [ 0.338] -3.63E-05 (4.9E-05) [-0.73894].65E-05 (4.9E-05) [ 0.53853] -0.199650 (0.03793) [-5.6339] Sock reurn volailiy - (-saisics) (P-value) C(Consan) (-saisics) (P-value) -6.130637 (17.0741) [-0.35906] 0.01716 (0.01875) [ 0.91537] -0.09109 (0.086) [-3.14] -9.13E-06 (3.1E-05) [-0.940] Deerminan resid covariance (dof adj.) 4.13E-07 Deerminan resid covariance 4.09E-07 Log likelihood 4995.73 Akaike informaion crierion -9.008557 Schwarz crierion -8.945158 Table-8 Pair-wise Granger Causaliy Tess beween sock reurn volailiy and rading volume Null Hypohesis: F-value P-values Sock eurn() does no Granger Cause Trading volume () Trading volume() does no Granger Cause Sock reurn ( ) 0.3097 0.14707 0.73404 0.8635 93
Inerdisciplinary Journal of esearch in Business ol. 1, Issue. 7, July 011(pp.81-95) Sock reurn volailiy( )does no Granger Cause Trading volume () Trading volume() does no Granger Cause Sock reurn volailiy( ) 6.8793* 315.837 * 0.00108 4.E-109 Lag(n) Table-9 ariance decomposiion of sock reurn and rading volume % of he movemen in he volume % of he movemen in he sock reurn explained by he shocks o: explained by shocks o: volume Sock reurn Sock reurn volume 1 100.0000 0.000000 99.9973 0.07070 99.99404 0.005956 99.9047 0.095733 3 99.95684 0.043157 99.90150 0.098497 4 99.94875 0.05149 99.89989 0.100105 5 99.93963 0.060371 99.89989 0.100113 6 99.93490 0.065101 99.89943 0.100568 7 99.93057 0.069431 99.89938 0.10063 8 99.9757 0.07430 99.89917 0.10087 9 99.9501 0.074986 99.89907 0.10097 10 99.9301 0.076988 99.89893 0.101066 Table-10 Impulse esponse funcion Period Sock reurn volailiy volume 1 0.000000-0.00056-0.00586-0.00030 3-0.016140 0.000107 4-0.010505-8.00E-05 5-0.01497 5.53E-06 6-0.010779-4.5E-05 7-0.011171-1.48E-05 8-0.01048 -.85E-05 9-0.010313-1.99E-05 10-0.009874 -.35E-05 Fig-3 esponse o Cholesky One S.D. Innovaions esponse of volume o volume esponse of volume o sock reurn.7.6.5.4.3..1.0 -.1 1 3 4 5 6 7 8 9 10.7.6.5.4.3..1.0 -.1 1 3 4 5 6 7 8 9 10.00 esponse of sock reurn o volume.00 esponse of sock reurn o sock reurn.016.016.01.01.008.008.004.004.000.000 -.004 1 3 4 5 6 7 8 9 10 -.004 1 3 4 5 6 7 8 9 10 94