Asian Journal of Finance & Accouning Causal Relaionship beween Macro-Economic Indicaors and Sock Marke in India Dr. Naliniprava ripahy Associae Professor (Finance), Indian Insiue of Managemen Shillong Meghalaya, PIN 793 014, India el: 91-364-30-8037 E-mail: nalini.607@rediffmail.com Received: May 16, 011 Acceped: November 13, 011 Published: December 1, 011 doi:10.596/ajfa.v3i1.633 URL: hp://dx.doi.org/10.596/ajfa.v3i1.633 Absrac his paper invesigaed he marke efficiency and causal relaionship beween seleced Macroeconomic variables and he Indian sock marke during he period January 005 o February 011 by using Ljung-Box Q es, Breusch-Godfrey LM es, Uni Roo es, Granger Causaliy es.he sudy confirms he presence of auocorrelaion in he Indian sock marke and macro economic variables which implies ha he marke fell ino form of Efficien Marke Hypohesis. Furher he Granger-causaliy es shows evidence of bidirecional relaionship beween ineres rae and sock marke, exchange rae and sock marke, inernaional sock marke and BSE volume, exchange rae and BSE volume. So i suggess ha any change of exchange rae, ineres rae and inernaional marke significanly influencing he sock marke in he economy and vice versa. he sudy also repored unidirecional causaliy running from inernaional sock marke o domesic sock marke, ineres rae, exchange rae and inflaion rae indicaing sizeable influence in he sock marke movemen in he considered period. he sudy poins ou ha he Indian sock marke is sensiive owards changing behavior of inernaional marke, exchange rae and ineres rae in he economy and hey can be used o predic sock marke price flucuaions. Keywords: Macroeconomic variables, Sock marke, Ljung-Box Q es, Uni Roo es, Granger-causaliy es JEL Classificaion: G1, G7, C3 08 www.macrohink.org/ajfa
Asian Journal of Finance & Accouning 1. Inroducion Over he pas few decades, he ineracion of share reurns and he macroeconomic variables has been a subjec of ineres among academicians and praciioners. Kaneko and Lee (1995), Lee (199), Fama (1981) deermined a posiive relaion beween sock reurns and real economic aciviy in US and Japanese sock markes bu he same relaion is no found in European and Souh Asian markes. Poon and aylor (1991) s sudy for he UK marke, Marinez and Rubio (1989) s sudy for he Spanish marke, and Gjerde and Saeem (1999) s sudy for he Norwegian marke have no implied a significan relaion beween sock reurns and macroeconomic variables. Mookerje and Yu (1997) s sudy on forecasing share prices for he Singapore case obained a resul ha money supply and exchange rae have an impac upon forecasing share prices. So he resuls are mixed. If sock prices accuraely reflec he underlying fundamenals, hen he sock prices should be employed as leading indicaors of fuure economic aciviies. herefore, he causal relaions among macroeconomic variables and sock prices are imporan in he formulaion of he naion s macroeconomic policy. Presenly he performance of Indian sock marke is analyzed carefully by large number of global players; his moivaes us for exploring research in Indian sock marke and macroeconomic indicaors o deermine he Indian sock marke efficiency o give new approach o he foreign invesors, policy makers, raders, domesic invesors and academic researchers. 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 relaionship beween macroeconomic variables and Indian sock marke by using Granger causaliy es for deermining wheher one ime series is useful for forecasing anoher. hirdly we use Uni Roo es and Box-Jenkins Auoregressive Inegraed Moving Average (ARIMA) ime-series process o deermine wheher Indian sock marke exhibis weak, semi-srong, or srong form of marke efficiency wih reference o macroeconomic variables is concerned o obain new insighs. herefore, he presen work improves he earlier sudies and offers a value addiion o he exising lieraure. he paper is organized as follows: Secion reviews previous lieraure Secion 3 describes he daa & mehodology used in he research. he resuls are discussed in Secion 4 and Secion 5 concludes he observaion.. Lieraure Review he dynamic relaionships beween macroeconomic variables and share reurns have been widely discussed and debaed. he informaional efficiency of major sock markes has been exensively examined hrough he sudy of causal relaions beween sock price indices and macroeconomic aggregaes. Kwon and Shin (1999) applied Engle-Granger co inegraion and he Granger-causaliy ess from he Vecor Error Correcion Model (VECM) and found ha he Korean sock marke is co inegraed wih a se of macroeconomic variables. However, using he Granger-causaliy es on macroeconomic variables and he Korean sock index, he auhors found ha he Korean sock index is no a leading indicaor for economic variables. Mayasmai and Koh (000) used he Johansen co inegraion es in he Vecor Error Correcion Model (VECM) and found ha he Singapore sock marke is co inegraed wih 09 www.macrohink.org/ajfa
Asian Journal of Finance & Accouning five macroeconomic variables. Muradoglu, Mein and Argac (001) examined he long-run relaionship beween sock reurns and hree moneary variables (overnigh ineres rae, money supply and foreign exchange rae) in urkey. hey poined ou ha he whole sample period (1988-1995) showed no co-inegraing relaionship beween sock prices and any of he moneary variables. his is also rue only for he firs sub-sample (1988-1989) bu all he variables were co inegraed wih sock prices for he second (1990-199) and hird sub-samples (1993-1995). Neverheless, in general, Ibrahim and Aziz (003), Booh and Booh (1997), Wongbanpo and Sharma (00), Chen (003), Chen e al. (005) and Mukherjee and Naka (1995) reveal ha he rae of inflaion, money growh, ineres raes, indusrial producion, reserves, and exchange raes are he mos popular significan facors in explaining he sock marke movemen. However, empirical sudies by Barrows and Naka (1994) conclude ha inflaion has negaive effecs on he sock marke. he exchange rae channel by Pan e al. (007) is consisen wih he flow oriened exchange rae model, inroduced by Dornbusch and Fisher (1980). hey affirm ha exchange rae movemens iniially affec he inernaional compeiiveness and rade posiion, followed by he real oupu of he counry, and finally affecs he curren and fuure cash flows of companies, which can be inferred from he sock price movemens. Donaas, P., & Vyauas B.,(009)analyzes he relaionships beween a group of macroeconomic variables and he Lihuanian sock marke index and reveals ha some macroeconomic variables lead Lihuanian sock marke reurns. 3. ime Series Daa and Mehodology Many financial ime series conain a uni roo, i.e. he series are non-saionary and i is generally acknowledged ha sock index and macroeconomic variables migh no be excepion. So he required ime series weekly daa have been colleced from he www.rbi.com and www.bse.com for a period of six years from January 005 o February 011.We have chosen he daa period 005 o 011 because during his period Indian sock markes have undergone subsanial policy changes characerised 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. hese changes have affeced he movemen in index and magniude of volume rades in he marke in differen ways. here are many macroeconomic variables which affecing he sock marke bu he mos prominen are ineres rae, inflaion rae, exchange rae and inernaional marke. A fall in ineres raes reduces he coss of borrowing and encourages firms for expansion wih he expecaion of generaing fuure expeced reurns for he firm. Furher significan amoun of socks are purchased wih borrowed money. So an increase in ineres raes will be more cosly for sock ransacions ha lead o reduce demand and affec he share price. Hence, changing ineres rae has greaer influence on sock marke variabiliy. So we have chosen 91-days reasury bill as proxy for shor erm ineres rae which is very popular shor-erm risk free insrumen in India. Similarly Wholesale Price Index focuses on he price of goods raded beween corporaions. I also moniors price movemens ha reflec supply and demand in indusry, manufacuring and consrucion. his helps in analyzing boh 10 www.macrohink.org/ajfa
Asian Journal of Finance & Accouning macroeconomic and microeconomic condiions. In India he changes of WPI is used o measure inflaion rae. I is believed ha change in WPI influences socks and fixed price markes. So we have chosen WPI as proxy for inflaion rae. hirdly, he S&P 500 is considered as he bes single gauge of he large cap U.S. equiies marke. he index includes 500 leading companies in leading indusries of he U.S. economy, capuring 75% coverage of U.S. equiies. I is also included in he index of leading indicaors. Furher, he "S&P 500"capures he changes in he prices of he index componens. I is noiced ha many imes variabiliy of Indian sock marke is happening due o inernaional marke facors. So S&P 500 is aken as proxy for inernaional marke index. Fourhly, change in exchange rae affecs he overseas operaional performances of firm which will affec is share price. So we have aken exchange rae one of he variables o deermine is impac on sock marke. Fifhly, 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. he BSE Index, SENSEX, is India's firs and mos popular Sock Marke benchmark index. So we have aken sensex as proxy for Indian sock marke. Lasly 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. he reurn is calculaed as he coninuously-compounded reurn using he closing price: R ln( P P 1 ) 100 % (1) Where ln (P ) denoes he naural logarihm of he closing price a ime. he heory behind ARMA esimaion is based on saionary ime series. A series is said o be saionary if he mean and auo co variances of he series do no depend on ime. Any series ha is no saionary is said o be non saionary. A common example of a non saionary series is he random walk. Serial correlaion coefficien es is a widely used procedure ha ess he relaionship beween reurns in he curren period wih hose in he previous period. If no significan auocorrelaion are found hen he series are expeced o follow a random walk. he Durbin-Wason saisics is a es for firs-order serial correlaion. he Durbin-Wason is a es of he hypohesis p=0 in he specificaion: u pu 1 () If here is no serial correlaion, he DW saisic will be around. he DW saisic will fall below if here is posiive serial correlaion (in he wors case, i will be near zero). If here is 11 www.macrohink.org/ajfa
Asian Journal of Finance & Accouning negaive correlaion, he saisics will lie somewhere beween and 4.However here are limiaions of he DW es as a es for serial correlaion. So wo oher ess of serial correlaion he Q-saisic and he Breusch-Godfrey LM es are preferred in mos applicaions. he bes alernaive is o use a es for auocorrelaion in a form of equaion, in which relaionship beween u and several of is lagged values a he same ime could be checked. Breusch Godfrey es is among he ess widely used for esing auocorrelaion of he lags up o r ' h order. u p u p u p u 1 1 3 3... p u r r v (3) v N( 0, v) Random walk hypohesis implies independen residuals and a uni roo.he auocorrelaions are easy o inerpre each one is he correlaion coefficien of he curren value of he series wih he series lagged a cerain number of periods. If he auocorrelaion funcion dies off smoohly a a geomeric rae, and he parial auocorrelaions were zero afer one lag, hen a firs-order auoregressive model is appropriae. Alernaively, if he auocorrelaions were zero afer one lag and he parial auocorrelaions declined geomerically, a firs-order moving average process would seem appropriae he auo correlaion of a series Y a lag K is esimaed by k ( y y)( y k k 1 ( y y) 1 y) (4) Where _ y is he sample mean of y. his is he correlaion coefficien for values of he series k periods apar. If 1 is non zero, i means ha he series is firs order serially correlaed if k dies off more or less geomerically wih increasing lag k, i is a sign ha he series obeys a low order auoregressive (AR) process. If k drops o zero afer a small number of lags; i is a sign ha he series obeys a low-order moving-average (MA) process. If he paern of auocorrelaion is one ha can be capured by an auo regression of order less han k, hen he parial auo correlaion a lag k will be close o zero. he parial auo correlaion a lag k recursively by 1 www.macrohink.org/ajfa
Asian Journal of Finance & Accouning k k 1 k 1 k j k 1, J 1 k 1 k j 1 k 1, j J 1 j (5) For K = 1 for K > 1 Where k is he esimaed auo correlaion a lag k and k, j k1, j k, k 1, k j, Q saisics is ofen issued, as a es of wheher he series is whie noise. he Q saisics a lag k is a es saisics for he null ha here is no auo correlaion up o order as is compued as k j QLB ( ) (6) j 1 j Where j is he jh auo correlaion and is he number of observaions. If he series is no based upon he resuls of ARIMA esimaion, hen under he null hypohesis, Q is asympoically disribued as a χ wih degrees of freedom equal o he number of auocorrelaions. If he series represens he residuals from ARIMA esimaion, he appropriae degrees of freedom should be adjused o represen he number of auocorrelaions. If here is no serial correlaion in he residuals, he auocorrelaions and parial auocorrelaions a all lags should be nearly zero, and all Q-saisics should be insignifican wih large p-values. If Q saisics measured found o be significan, i can be said ha he marke does no follow random walk. Knowledge of non-saionariy of he ime series is significan in he modelling of economic relaionships because sandard saisical echniques ha assume saionariy may give invalid inferences in he presence of sochasic rends. In case of non-saionariy daa, ordinary leas squares can produce spurious resuls. herefore, prior o modelling any relaionship, non-saionariy mus be esed. he daa considered for he sudy is ime series, which is non-saionary. For applicaion of Granger Causaliy he iniial sep in he esimaion involves he deerminaion of he imes series propery of each variable individually by conducing uni roo ess. Considering a simple AR (1) process: y p y x' 1 (7) Where x are opional exogenous regressors which may consis of consan, or a consan and rend, p and δ are parameers o be esimaed, and he are assumed o be whie noise. If,p1, y is a nonsaionary series and he variance of increases wih ime and approaches 13 www.macrohink.org/ajfa
Asian Journal of Finance & Accouning infiniy. If,p< 1.y is a (rend-)saionary series. hus, he hypohesis of (rend-)saionariy can be evaluaed by esing wheher he absolue value of p is sricly less han one. he null hypohesis H o : p=1 agains he one-sided alernaiveh 1 : p<1. In some cases, he null is esed agains a poin alernaive. he mos popular uni roo res is he ADF es. he sandard DF es is carried ou afer subracing y -1 from boh he sides of he equaion: y y 1 x ' (8) Where α= p-1. he null and alernaive hypoheses is wrien as H o : α=0 H 1 : α<0 he simply Dickey Fuller uni roo es includes AR (1) process and described valid If he series is correlaed a higher order lags, he assumpion of whie noise disurbances is violaed. he Augmened Dickey-Fuller (ADF) es consrucs a parameric correcion for higher-order correlaion by assuming ha he y series follows an AR (1 ) process and adding p lagged difference erms of he dependen variable y o he righ hand side of he es regression: y y x ' y y... y 1 1 1 p p v (9) Said and Dickey (1984) demonsrae ha he ADF es is asympoically valid in he presence of a moving average (MA) componen, provided ha sufficien lagged difference erms are included in he es regression. 4. Dickey-Fuller es wih GLS De rending (DFGLS) Ellio e al. (1996) propose a simple modificaion of he ADF ess in which he daa are de rended so ha explanaory variables are aken ou of he daa prior o running he es regression. ERS (1996) obain he asympoic power envelope for uni-roo ess by analyzing he sequence of Neyman-Pearson ess of he null hypohesis H 0 : p= 1 agains he local alernaive H a :p=1+c /, wherec<0. Based on asympoic power calculaion, ERS show ha a modified Dickey-Fuller es, called he DF-GLS es, can achieve a subsanial gain in power over radiional uni-roo ess. he DF-GLS es ha allows for a linear ime rend is based on he following regression: p ( 1 L) y d y d l y d (1 ) v (10) 1 j 1 j 1 Where v is an error erm and y d is he locally de rended daa process under he local 14 www.macrohink.org/ajfa
Asian Journal of Finance & Accouning alernaive of p 1 c / is given by y y z Wih β being he leas squares regression coefficien of y on z, for which y =[y 1,( 1-p L)y...(1-p L)y ] and Z =[Z 1, (1-p L)Z,.....(1-p L) Z 1 ]he DF-GLS saisic is given by he -raio, esing H 0 : o =0 agains Ha: 0 < 0.ERS recommend ha he parameer of defining he local alernaive, c, be se equal o -13.5.For he es wihou a ime rend, denoed by DF-GLS., i involves he same procedure as he DF-GLS es, excep ha y d is replaced wih he locally demeaned series y d and z =1. In his case, he use of c =-7 is recommended. Phillips-Perron(PP)es Phillips and Perron (1988) developed a number of uni roo ess ha have become popular in he analysis of financial ime series. he Phillips-Perron (PP) uni roo ess differ from he ADF ess mainly in how hey deal wih serial correlaion and heeroskedasiciy in he errors. In paricular, where he ADF ess use a parameric auo regression o approximae he ARMA srucure of he errors in he es regression, he PP ess ignore any serial correlaion in he es regression. he es regression for he PP ess is y ' D y u (11) 1 where u is I(0) and may be heeroskedasic. he PP ess correc for any serial correlaion and heeroskedasiciy in he errors u of he es regression by direcly modifying he es saisics π=0 and ˆπ. hese modified saisics, denoed Z and Zπ, are given by Z ˆ ˆ 1 / 1 SE ( ˆ ) ( ). 0 ( ).( ) (1) ˆ ˆ ˆ 1. SE ( ˆ ) Z ˆ ( ˆ ˆ ) (13) ˆ he ermsˆ and ˆ are consisen esimaes of he variance parameers 1 lim E x 1 u 15 www.macrohink.org/ajfa
Asian Journal of Finance & Accouning lim E x 1 1 S where S u 1.he sample variance of he leas squares residual û is a consisen esimae of σ, and he Newey-Wes long-run variance esimae of u using û is a consisen esimae of λ. Under he null hypohesis ha π = 0, he PP Z and Zπ saisics have he same asympoic disribuions as he ADF -saisic and normalized bias saisics. One advanage of he PP ess over he ADF ess is ha he PP ess are robus o general forms of heeroskedasiciy in he error erm u. Anoher advanage is ha he user does no have o specify a lag lengh for he es regression. 5. KPSS (Kwiakowski, Phillips, Schmid, and Shin) es 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. he 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. he KPSS saisic is based on he he residuals from he OLS regression of y on he exogenous variables x y 1 x (14) he LM saisics is given by: LM 1 s / (15) Where, is an esimaor for he error variance. his 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. he 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. 6. Ng and Perron (NP) ess Ng and Perron (001) use he GLS de rending procedure of ERS o creae efficien versions of he modified PP ess of Perron and Ng (1996). hese efficien modified PP ess do no exhibi he severe size disorions of he PP ess for errors wih large negaive MA or AR roos, and hey can have subsanially higher power han he PP ess. Especially, when φ is close o uniy. 16 www.macrohink.org/ajfa
Asian Journal of Finance & Accouning Using he GLS de rended daa y d, he efficien modified PP ess are defined as MZ ( 1 y d )( 1 y d 1 ) 1 (16) MSB ( 1 d y 1 / ) 1 / (17) MZ MZ x MSB (18) he saisics MZ and MZ are efficien versions of he PP Zα and Z ess, ha have much smaller size disorions in he presence of negaive moving average errors. Ng and Perron derive he asympoic disribuions of hese saisics under he local alernaive φ = 1 c/ for D = 1 and D = (1, ). In paricular, hey show ha he asympoic disribuion of MZ is he same as he DF-GLS -es. 7. Granger - Causaliy es he dynamic linkage is examined using he concep of Granger s (1969) causaliy. he Granger ype causaliy procedure (Granger, 1969, 1988) is applied o deermine he direcion of causaion among he variables. he causaliy procedure is conduced based on bi-variae sysem (x, y). Formally, a ime series X, Granger-causes anoher ime series Y if series Y can be prediced beer by using pas values of (X, Y ) han by using only he hisorical values of Y. In oher words, X fails o Granger cause Y if for all M>O he condiional probabiliy disribuion of Y +m given (Y, Y -1 ) is he same as he condiional probabiliy disribuion of Y +m given boh (Y, Y -1,.) and (X, Y -1,.). ha is X, does no Granger cause Y if Where P r denoes condiional probabiliy, Ψ is he informaion se a ime on pas values Y, and Ω is he informaion se conaining values of boh X and Y up o ime poin. esing causal relaions beween wo saionary series X and Y can be based on he following bi- variae auo regression (Granger 1969). (19) 17 www.macrohink.org/ajfa
Asian Journal of Finance & Accouning (0) Where P is a suiably chosen posiive ineger; k s and β k s, K = 0, 1, -----, p are consans; U and V are usually disurbance erms wih zero means and finie variance. he null hypohesis ha X does no Granger cause Y is rejeced if he β k s, K>0 in equaion are joinly significanly differen from zero using a sandard join es (e.g., an F es). Similarly, Y Granger causes if he k s, K>0 are joinly differen from zero. 8. Empirical Analysis 8.1 Descripive Saisics he summary saisics for BSE Sensex, BSE volume change, 91-day -bill rae, S&P 500, exchange rae, and WPI are given in able-1. All reurns are calculaed as he firs difference of he log of he weekly closing price. he mean of he BSE Sensex is -0.059856. he volailiy of he index is 1.46307. he mean of he 91-day -bill aucion rae is -0. 3391. he S&P 500 reurns are -0.10676. he exchange rae is 0.01961; and he mean of wholesale price index is 0.153914.he kurosis for all he aforemenioned facors is more han 3 (excess kurosis), hus hey are lepokuric, i.e., he frequency disribuion assigns a higher probabiliy o reurns around zero as well as very high posiive and negaive reurns. he Jarque-Bera saisic for all he 6 variables is significanly greaer han zero (due o he lepokuric daa). hus, Jarque-Bera saisics shows ha all he series are lepokuric, exhibi non-normaliy and indicae he presence of Heeroscedasiciy. able 1. Descripive Saisics Variable Mean Sd. Dev. Skewness Kurosis Jarque-Bera Probabiliy BSE Reurn -0.059856 1.46307 0.367803 15.64054 14.99 0.000000 BSE Volume 0.035957 7.048135-0.481634 1.0001 108.181 0.000000 91-Day reasury Bill Rae -0.3391 8.907419 -.03009 31.8345 10783.80 0.000000 S&P 500 Reurn -0.10676 1.5119-0.046508 1.66397 133.671 0.000000 Exchange Rae 0.01961 0.506510-0.950935.0149 483.475 0.000000 WPI 0.153914 3.53606 17.54097 310.9098 168518. 0.000000 able. Durbin-Wason ess Variable Durbin-Wason sa F-saisic Prob(F-saisic) BSE sensex 1.996156 387.6970 0.000000 BSE rading Volume 1.987563 134.1877 0.000000 91-Day reasury Bill.01978 93.39633 0.000000 S&P 500 Reurn 1.99787 366.804 0.000000 Exchange Rae 1.987834 355.348 0.000000 WPI.000006 315.0488 0.000000 18 www.macrohink.org/ajfa
Asian Journal of Finance & Accouning able- repored he Durbin-Wason saisics for all he variables and hey are all wihin he range of 1.9 and., which is indicaive of he absence of firs order serial correlaion. Hence he resul can be relied upon o es uni roo. DW es, which is a es for serial correlaions, has been used in he pas bu he explanaory power of he DW can be quesioned on he basis ha he DW only looks a he serial correlaions on one lags as such may no be appropriae es for he daily daa. So for marke efficiency we have used uni roo es of saionariy. Auocorrelaion is useful for finding repeaing paerns in a signal, such as deermining he presence of a periodic signal. he auo correlaion and parial correlaion funcions (ACF and PACFs) of he series of BSE sensex, rading volume, 91-days reasury bill, S&P 500, Exchange rae and WPI are presened in he able 3, fig-1 and fig-. able 3. Auo Correlaion and Parial Auo correlaion Lag AC PAC Q-Sa Prob 1-0.106-0.106 3.643 0.057-0.014-0.06 3.691 0.158 3-0.061-0.066 4.9033 0.179 4 0.084 0.071 7.08 0.16 5-0.089-0.077 9.7881 0.081 6-0.095-0.115 1.71 0.048 7 0.036 0.019 13.138 0.069 8 0.059 0.046 14.67 0.075 9-0.088-0.079 16.789 0.05 10-0.086-0.095 19.5 0.037 11 0.070 0.034 0.88 0.035 1 0.038 0.05 1.99 0.046 13 0.165 0.197 30.388 0.004 14-0.093-0.045 33.93 0.003 15 0.097 0.054 36.468 0.00 16 0.087 0.15 39.04 0.001 17 0.034 0.068 39.416 0.00 18-0.11-0.163 54.56 0.000 19 www.macrohink.org/ajfa
Asian Journal of Finance & Accouning Auocorrelaion Funcion for C1 (wih 5% significance limis for he auocorrelaions) 1.0 0.8 0.6 Auocorrelaion 0.4 0. 0.0-0. -0.4-0.6-0.8-1.0 4 6 8 10 Lag 1 14 16 18 Figure 1 Parial Auocorrelaion Funcion for C1 (wih 5% significance limis for he parial auocorrelaions) 1.0 0.8 Parial Auocorrelaion 0.6 0.4 0. 0.0-0. -0.4-0.6-0.8-1.0 4 6 8 10 Lag 1 14 16 18 Figure 0 www.macrohink.org/ajfa
Asian Journal of Finance & Accouning he resuls of he es presened in able-3 ha Q-saisics are significan a almos all lags, indicaing significan serial correlaion in he residuals and he null hypohesis of weak-form marke efficiency is rejeced. I confirms he presence of auocorrelaion in he Indian sock marke and macro economic variables, which implies ha he marke does no follow random walk and fell ino a form of Efficien Marke Hypohesis. However, he heory of sock marke behaviour and anomalies presens evidence agains he EMH. he sudy here suggess ha marke raionally process informaion so ha marke efficiency holds bu significan auocorrelaion may arises from marke fricion. I indicaes ha marke fricions may be due o dependence on weekly reurns of macroeconomic variables. able 4. Breusch-Godfrey Serial Correlaion LM es F-saisic 9.71466 Probabiliy 0.000000 Obs*R-squared 34.16653 Probabiliy 0.000001 Breusch-Godfrey Serial Correlaion LM es is presened in able 4 and he es rejecs he hypohesis of no serial correlaion. he Q-saisic and he LM es boh indicae ha he residuals are serially correlaed and presence of efficien a he weak-form. able 5. Uni Roo es Variable ADF es DF-GLS es PP es KPSS es Ng-Perron es BSE sensex -19.6900* -19.68534* -19.97884* 0.157864* 0.0569* BSE rading Volume -13.14690* -13.134* -.94935* 0.048900* 0.031* 91-Day reasury Bill -10.8667* -16.9958* -37.4155* 0.3414* 0.05055* S&P 500 Reurn -19.13845* -19.1985* -19.13375* 0.57038* 0.05604* Exchange Rae -18.85006* -18.87945* -18.8371* 0.08894* 0.05635* WPI -17.7496* -17.75305* -17.74960* 0.18415* 0.0565* Asympoic Criical values* 1% level -3.458973 -.5777-3.458470 0.739000 0.17400 5% level -.87409-1.94187 -.873809 0.463000 0.3300 10% level -.57350-1.616030 -.573384 0.347000 0.7500 he sudy here employs he uni roo es o examine he ime series properies of concerned variables. Uni roo es describes wheher a series is saionary or non-saionary. For he es of uni roo he presen sudy employees he Augmened Dickey Fuller es, DF-GLS es, PP es, KPSS es and Ng-Perron es. hese ess are used o measure he saionariy of ime series daa which in urn ells wheher regression can be done on he daa or no. I is apparen from able-5 ha he resuls are saisically significan and less han criical values. So he resuls of all ess are consisen suggesing ha hese markes are no weak form efficien. I recommends ha he reurn series of all variable does no follow random walk model and he sock reurns display predicable behaviour. On observing he oupus i is seen ha he es saisic for all 6 variables are less han he criical values a 1%, 5% and 10% confidence level. So, he null hypohesis is rejeced and 1 www.macrohink.org/ajfa
Asian Journal of Finance & Accouning he daa is found o be saionary. herefore, we can apply Granger causaliy es which requires he daa o be saionary in order o avoid geing spurious resuls. able 6. Granger Causaliy es Null Hypohesis: F-Saisic Probabiliy BSE Volume does no Granger Cause BSE SENSEX BSE SENSEX does no Granger Cause BSE Volume 91-Day reasury Bill Rae does no Granger Cause BSE SENSEX BSE Reurn does no Granger Cause 91-Day reasury Bill Rae S&P 500 Reurn does no Granger Cause BSE SENSEX BSE SENSEX does no Granger Cause S&P 500 Reurn Exchange Rae does no Granger Cause BSE SENSEX BSE SENSEX does no Granger Cause Exchange Rae WPI does no Granger Cause BSE SENSEX BSE SENSEX does no Granger Cause WPI 91-Day reasury Bill Rae does no Granger Cause BSE Volume BSE Volume does no Granger Cause 91-Day reasury Bill Rae S&P 500 Reurn does no Granger Cause BSE Volume BSE Volume does no Granger Cause S&P 500 Reurn Exchange Rae does no Granger Cause BSE Volume BSE Volume does no Granger Cause Exchange Rae WPI does no Granger Cause BSE Volume BSE Volume does no Granger Cause WPI S&P 500 Reurn does no Granger Cause 91-Day reasury Bill Rae 91-Day reasury Bill Rae does no Granger Cause S&P 500 Reurn Exchange Rae does no Granger Cause 91-Day reasury Bill Rae 91-Day reasury Bill Rae does no Granger Cause Exchange Rae WPI does no Granger Cause 91-Day reasury Bill Rae 91-Day reasury Bill Rae does no Granger Cause WPI Exchange Rae does no Granger Cause S&P 500 Reurn S&P 500 Reurn does no Granger Cause Exchange Rae WPI does no Granger Cause S&P 500 Reurn S&P 500 Reurn does no Granger Cause WPI WPI does no Granger Cause Exchange Rae Exchange Rae does no Granger Cause WPI * Null hypohesis rejeced a 1% significance level **Null hypohesis rejeced a 5% significance level *** Null hypohesis rejeced a 10% significance level 17.1176* 0.4798.6497** 4.89076* 35.6953* 1.66070 7.5360* 4.3873* 6.78080* 1.43551 3.41154** 0.97177 4.1783* 3.4498** 7.36818* 16.7148* 0.436.10149 7.47631* 0.6556 0.35730.1736 0.95018 0.31551 1.5943 11.7473* 1.88505 3.99548* 7.00005* 1.0144 8.9E-08 0.651 0.077 0.00811 1.1E-14 0.19169 0.00064 0.0157 0.00131 0.3957 0.0343 0.37956 0.0160 0.04030 0.00075 1.3E-07 0.79916 0.1401 0.00067 0.76695 0.69985 0.11554 0.38780 0.7965 0.0471 1.E-05 0.15356 0.01935 0.00106 0.36453 he Granger-causaliy es is conduced o sudy he causal relaionship beween macro economic variables and he Indian sock marke. able-6 repored pair wise Granger causaliy es resuls wih lags as wo lag is an appropriae lag order chooses in erms of he Akaike Informaion Crieria (AIC) for he full sample period. BSE rading volume, reasury bill rae, www.macrohink.org/ajfa
Asian Journal of Finance & Accouning S&P 500, Exchange rae, and WPI are found o be he mos imporan variable in deermining sock marke reurn. he repored F-values suggess ha here is a unidirecional causaliy beween rading volume and sock marke, inernaional sock marke and domesic sock marke, inflaion rae and sock marke, ineres rae and rading volume, inernaional sock marke and ineres rae, inernaional sock marke and exchange rae, inernaional sock marke and inflaion rae, inflaion rae and exchange rae. his implies ha inernaional marke influence he domesic sock marke, exchange rae, inflaion rae and ineres rae. Apar of his, any changes in rading volume and inflaion rae also affecing sock marke. I is also found from he able-6 ha here is bidirecional relaionship beween ineres rae and sock marke, exchange rae and sock marke, inernaional sock marke and BSE volume, exchange rae and BSE volume. So i suggess ha exchange rae and ineres rae are influencing he sock marke and any variaion in sock marke also influencing he exchange rae and ineres rae in he economy. Also i is experimened ha variabiliy of inernaional marke and exchange rae is affecing rading volume changes in he sock marke. Again i is observed from he able-6 ha here is no apparen causaliy beween inflaion rae and rading volume, ineres rae and exchange rae, ineres rae and inflaion rae. 9. Concluding observaion his sudy examines he relaionship beween he sock marke and a se of macroeconomic variables during he period of January 005 o February 011. he ime series daa se employed in his sudy comprises he weekly observaions of he BSE Sensex, WPI, reasury bill rae, Exchange rae, S&P 500 and BSE rading volume. he sudy used Ljung-Box Q saisics and Breusch-Godfrey Serial Correlaion LM es o deermine he auo correlaion of all variables. he sudy confirms he presence of auocorrelaion in he Indian sock marke and macro economic variables. he sudy also used he Granger causaliy es o deermine he causal effec relaionship beween he BSE Sensex wih macro economic variables. Saisical inferences are drawn from he daa by means of significance ess and bidirecional causaliy is seen beween inflaion rae and sock marke, exchange rae and sock marke, ineres rae and sock marke, inernaional sock marke and BSE volume, Exchange Rae and BSE volume. Similarly unidirecional causaliy is found beween inernaional sock marke and domesic sock marke, inernaional sock marke and exchange rae, inernaional sock marke and inflaion rae, inernaional sock marke and ineres rae. So he sudy suggess ha Indian sock marke is influenced by inflaion rae, exchange rae and ineres rae in he economy. So hey can be used o predic sock marke price flucuaions. he sudy also found ha variabiliy of inernaional marke and exchange rae is affecing rading volume change in he sock marke in he economy. Furher he sudy reveals ha Indian sock markes are no weak form efficien. So i implies ha he sensible invesor in India can aain abnormal reurns using hisorical daa of sock prices, and macroeconomic indicaors. his may enable he raders and invesors o work ou profiable sraegy for rading or o ake invesmen decision. One of he limiaions of he sudy is ha we have used five macroeconomic variables only, so furher research needs o be explored by including more macroeconomic variables o know he relaionships beween hese facors and he naure of sock marke volailiy. Secondly, i 3 www.macrohink.org/ajfa
Asian Journal of Finance & Accouning is also quie possible ha he macroeconomic variables have differen impac on sock marke volailiy depending on he rading mechanisms and regulaory environmens. References Barrows CW, & Naka A. (1994). Use of Macroeconomic Variables o Evaluae Seleced Hospialiy Sock Reurns in he U.S. Inernaional Journal of Hospial Managemen, 13: 119-18. hp://dx.doi.org/10.1016/078-4319(94)90033-7 Booh JR, Booh LC. (1997). Economic Facors, Moneary Policy and Expeced Reurns on Sock and Bonds Economic Review. Federal Reserve Bank of San Francisco Economic Review, :3-4. Chen MH. (003). Risk, Reurn and CAPM. Quarerly Review of Economics and Finance, 43: 369-393. hp://dx.doi.org/10.1016/s106-9769(0)0015-4 Chen MH, Kim WG, & Kim HJ. (005). Macro and Non-Macro Explanaory Facors of Chinese Hoel Sock Reurns. Inernaional Journal of Hospial Managemen, 4: 43-58. Chen SJ, Roll F, & Ross SA. (1986). Economic Forces and he Sock Marke. Journal of Business, 59(3): 505-53. Dickey, D.A., & W.A. Fuller. (1979). Disribuion of he Esimaion for Auoregressive ime series wih a Uni Roo. Journal of American Saisical Associaion, 79: 355-367 Dornbusch R, & Fisher S. (1980). Exchange Raes and he Curren Accoun. he American Economic Review, 70: 690-971. Donaas Pilinkus, & Vyauas Boguslauskas. (009). he Shor-Run Relaionship beween Sock Marke Prices and Macroeconomic Variables in Lihuania: An Applicaion of he Impulse Response Funcion Inzinerine Ekonomika. Engineering Economics, 139 785 Ellio, G. Rohenberg,. J., & Sock, J. H. (1996). Efficien ess for an Auoregressive Uni Roo. Economerica, 64: 813-836 Fama EF. (1981). Sock Reurns, Real Aciviy, Inflaion and Money. he American Economic Review, 71: 115-46. hp://links.jsor.org/sici?sici=00-108%8199009%945%3a4%3c1089%3asrerar% 3E.0.CO%3B-1 Granger, C.W.J. (1988). Some Recen Developmens in a Concep of Causaliy. Journal of Economerics, 39: 13-8 Gjerde O, & Saeem F. (1999). Causal Relaions among Sock Reurns and Macroeconomic Variables in a Small. Open Economy Journal of Inernaional Finance Marke, 9: 61-74. hp://www.sciencedirec.com/science/aricle/b6vg-3v8cd7-4/1/91e7b4fcf1b115fc45ed5 19689fea8 Granger, C.W.J. (1969). Invesigaing Causal Relaions by Economeric Models and Cross-specral Mehods. Economerica, 37: 48-438 4 www.macrohink.org/ajfa
Asian Journal of Finance & Accouning Humpe, A., & P. Macmillan. (009). Can macroeconomic variables explain long-erm sock marke movemens? A comparison of he US and Japan. Applied Financial Economics, Volume 19() 111-19 Ibrahim, M. H. & Aziz, H. (003). Macroeconomic variable and he Malaysian equiy marke: A view hrough rolling subsamples. Journal of Economic Sudies, 30: 1: 6-7. hp://dx.doi.org/10.1108/0144358031045541 Ibrahim, M. H. (1999). Macroeconomic variables and sock prices in Malaysia: An empirical analysis. Asian Economic Journal, 13: : 19-31. hp://dx.doi.org/10.1111/1467-8381.0008 Kaneko, Lee BS. (1995). Relaive Imporance of Economic Facors in he U.S. and Japanese Sock Markes. Journal of he Japanese and Inernaional Economies, 9: 90-307 Kwon, C. S. & Shin,.S. (1999). Co-inegraion and causaliy beween macroeconomic variables and sock marke reurns. Global Finance Journal, 10: 1: 71-81. hp://dx.doi.org/10.1016/s1044-083(99)00006-x Kwiakowski, D., P. C. B. Phillips, P. Schmid, & Y. Shin. (199). esing he null hypohesis of rend saionariy. Journal of Economerics, Volume 54, 159-178. hp://dx.doi.org/10.1016/0304-4076(9)90104-y Lee B. (199). Causal Relaions among Sock Reurns, Ineres Raes, Real Aciviy and Inflaion. Journal of Finance, 47: 1591-1603. Maysami, R. C. & Koh,.S. (000) A vecor error correcion model of he Singapore sock marke. Inernaional Review of Economics and Finance, 9: 79-96. hp://dx.doi.org/10.1016/s1059-0560(99)0004-8 Mokerjee e al. (1997). Macroeconomic Variables and Sock Prices in a Small Open Economy: he Case of Singapore. Pacific-Basin Finance Journal, 5:377-388. hp://dx.doi.org/10.1016/s097-538x(96)0009-7 Mukherjee,. K. & Naka, A. (1995). Dynamic relaions beween macroeconomic variables and he Japanese sock marke: an applicaion of a vecor error correcion model. Journal of Financial Research, 18: : 3-37. Muradoglu, G.e.al. (001). Is here a long run relaionship beween sock reurns and moneary variables: Evidence from an emerging marke? Applied Financial Economics, vol. 11 (6): 641-49. hp://dx.doi.org/10.1080/09603100110094411 Marinez, M.A., & G. Rubio. (1989). Arbirage Pricing wih Macroeconomic Variables: An Empirical Invesigaion using Spanish Daa, Working Paper, Universidad del Pais Vasco. Ng, S., & Perron, P. (001). Lag Lengh Selecion and he Consrucion of Uni Roo ess wih Good Size and Power. Economerica, 69 6: 1519-1554 Pan Ming-Shiun e al. (007). Dynamic Linkages beween Exchange Raes and Sock Prices: Evidence from Eas Asian Markes. Inernaional Review of Economics and Finance, I 5 www.macrohink.org/ajfa
Asian Journal of Finance & Accouning 16:503-50. Pearce, D. K., & Roley, V.V. (1988). Firm characerisics, unanicipaed inflaion, and sock reurns. Journal of Finance, 43: 965-981. hp://dx.doi.org/10.307/38146 Poon S, aylor J. (1991). Macroeconomic Facors and he U.K. Sock Marke. Journal of Business Finance & Accouning, 18(5): 619-636. Perron, P., & S. Ng. (1996). Useful Modificaions o Some Uni Roo ess wih Dependen Errors and heir Local Asympoic Properies. Review of Economic Sudies, 63, 435-463. hp://dx.doi.org/10.307/97890 Phillips, R. C. B., & Perron. (1988). esing for a Uni Roo in ime Series Regression Biomerika, 335-346 Ross, S. A. (1976). he arbirage heory of capial asses. Journal of Economic heory, December 341-360. hp://dx.doi.org/10.1016/00-0531(76)90046-6 Said, E., & David A. Dickey (1984) esing for uni roos in auoregressive-moving average models of unknown order. Biomerika 71 (3): 599-607 hp://dx.doi.org/10.1093/biome/71.3.599 Wongbanpo, P., & Sharma, S. C. (00). Sock marke and macroeconomic fundamenal dynamic ineracions: ASEAN-5 counries. Journal of Asian Economics, 13: 7-51 hp://dx.doi.org/10.1016/s1049-0078(01)00111-7 6 www.macrohink.org/ajfa