Relationship between Stock Returns and Trading Volume: Domestic and Cross-Country Evidence in Asian Stock Markets

Transcription

1 Proceedings of he 2013 Inernaional Conference on Economics and Business Adminisraion Relaionship beween Sock Reurns and Trading olume: Domesic and Cross-Counry Evidence in Asian Sock Markes Ki-Hong Choi Deparmen of Economics Pusan Naional Universiy Busan, Korea Sang Hoon Kang Deparmen of Business Adminisraion Pusan Naional Universiy Busan, Korea Seong-Min Yoon (Corresponding Auhor) Deparmen of Economics Pusan Naional Universiy Busan, Korea Absrac - We examined he effecs of rading volume on he persisence of he ime-varying condiional volailiy of reurns and he dynamic relaions beween rading volume and reurns (and volailiy) for boh domesic and cross-counry markes. We considered daily prices and rading volume in four Asian sock exchanges (Korea, Japan, China, and Hong Kong). For he analysis, we used he GARCH model, which includes rading volume. To analyze wheher rading volume precedes sock reurns, or vice versa, we used he Granger causaliy es. Our maor findings are as follows. Firs, he inclusion of rading volume in he GARCH model does no reduce he persisence of condiional variance of each of he four sock markes. Second, regarding cross-counry relaionships, Hong Kong financial marke variables, in paricular Hong Kong rading volume, have exensive predicive power for he financial markes of Japan and Korea. Third, cross-counry ineracions are weak, and Japan s inernaional sock marke is subsanially influenced by marke variables ouside of he sock markes of Korea, Hong Kong, and China. Keywords: Causaliy, Persisence, Trading volume, olailiy I. INTRODUCION There is much ineres in he relaionship beween sock reurns and rading volume. The imporance of rading volume and is impac on he volailiy of financial asses is well known in finance lieraure. A number of sudies on he relaionships beween rading volume and reurns (and volailiy) in domesic markes have been conduced. However, cross-counry markes remain less explored. Mos previous empirical research has used daa from inernaional markes, bu relaively few sudies have been conduced on Asian sock exchanges. In he presen sudy, we examined he causal relaionships among sock marke reurns, rading volume, and volailiy in four Asian sock markes: hose of Korea, Japan, Hong Kong, and China. We considered each domesic sock marke individually as well as cross-counry effecs. In paricular, we invesigaed wheher rading volume as a proxy for informaion is useful for improving predicions of reurns and reurn volailiy. The remainder of his paper is organized as follows. A lieraure review is presened in Secion 2. Secion 3 presens our sample daa. A descripion of our mehodology and empirical resuls are presened in Secions 4 and 5, respecively. Secion 6 concludes he paper. II. LITERATURE REIEW Economiss have long been ineresed in sudying he relaionships beween sock reurn volailiy and rading volume. The mixing of disribuion hypohesis (MDH) links changes in price, volume, and rae of informaion flow (Clack 1973, Epps and Epps 1976, Harris 1986, Morgan 1976, Tauchen and Pis 1984) and implies a posiive relaionship beween rading volume and sock reurns. This relaionship is a funcion of a mixing variable defined as he rae of informaion. Lamoureux and Lasrapes (1990), esing he relaionship beween volume and volailiy for a number of acively raded socks in he Unied Saes, used conemporaneous rading volume as an explanaory variable in he variance equaion and found ha he inclusion of volume eliminaed he persisence of volailiy. Gallo and Pacini (2000), using daa on 10 acively raded U.S. socks from 1985 o 1995, found ha persisence decreased when rading volume was used in he condiional variance 33

2 Proceedings of he 2013 Inernaional Conference on Economics and Business Adminisraion equaion. Foser (1995) esed he predicions of MDH for he oil fuures marke from 1990 o 1994 and found ha volume and volailiy were largely conemporaneously relaed and ha boh were driven by he same facor, which is assumed o be informaion arrival. Alsubie and Naand (2009) esed he effec of rading volume on he persisence of he condiional volailiy of reurns in he Saudi sock marke. They idenified good proxies for informaion flow and conemporaneous volume. However, no all sudies have suppored he conemporaneous relaionship beween sock reurn volailiy and rading volume. Copeland (1976), Mores (1981), and Jennings, Sarks and Fellingham (1981) derived he sequenial informaion arrival hypohesis (SIAH), which suggess a lead-lag relaionship beween volume and volailiy only in he presence of informaion. Sharma e al. (1996) invesigaed he relaionship beween rading volume and GARCH for he New York Sock Exchange (NYSE) index from 1986 o They found ha rading volume did no compleely explain he GARCH effec, and concluded ha while rading volume migh be a good proxy for informaion arrival abou individual firms, i is no rue for he marke as a whole. Lee (2009) invesigaed he relaionship beween rading volume and volailiy on Korean markes using he hreshold GARCH (TGARCH) model and found ha here was asymmeric volailiy in he Korea Composie Sock Price Index (KOSPI) and on he Korean Securiies Dealers Auomaed Quoaions (KOSDAQ) marke, bu concluded ha inclusion of rading volume did no reduce volailiy persisence in he condiional variance equaion. Kim and Kim (2008) invesigaed he relaionship beween reurn volailiy and volume of he KOSPI 200 fuures index using he GJR GARCH model. They idenified volailiy persisence, asymmeric responses o informaion arrival, and a relaionship beween reurn volailiy and volume. Some sudies have invesigaed he dynamic relaionship beween rading volume and reurns and/or volailiy. For example, Wang (1994) analyzed volume and reurns and found ha volume may provide informaion abou expeced fuure reurns. Chordia and Swaminahan (2000) examined rading volume and he predicabiliy of shor-erm sock reurns, and found ha daily reurns of socks wih high rading volume lead he daily reurns of socks wih low rading volume. Chen, Firh and Rui (2001) examined all hree facors and found ha rading volume conribues some informaion o he reurns process; hey also repored persisence in volailiy even afer hey incorporaed he effecs of conemporaneous and lagged volumes. Lee and Rui (2002) examined he dynamic relaionships beween sock marke reurns/volailiy and rading volume using he daa for he hree larges sock markes in he world: New York, Tokyo, and London. They considered each domesic marke individually as well as cross-counry effecs, and found ha rading volume does no lead o Granger cause reurns in each marke, bu here is a posiive feedback relaionship beween volume and volailiy in all hree markes. Regarding cross-counry effecs, hey found ha US financial variables have exensive predicive power for he oher markes. III. METHODOLOGY A. GARCH model In general, he ARCH model of Engle (1982) and he GARCH model of Bollerslev (1986) are he mos popular ools for capuring he volailiy dynamics of financial ime series. In he presen sudy, we used GARCH, which is paricularly useful because i makes curren condiional variance dependen on lags in is previous condiional variance. To es he effecs of rading volume on sock reurn volailiy, he following GARCH (1,1) model was employed: r = µ + λ + ε (1) ε z h, z ~ N(0,1) (2) h = 2 = ω + αε 1 + βh 1 + θ (3) r is he daily sock reurns, µ denoes he mean where of he reurns, and is volume change, which is used as a proxy for informaion arrival o he marke. Equaion (3) specifies condiional variance as a funcion of mean 2 volailiy ω, where ε 1 is he lag in he squared residual of he mean (he ARCH erm) and provides informaion abou volailiy clusering, and h 1 is he previously forecased variance (he GARCH erm). The sum ( α + β) is a measure of he persisence of a shock o he variance. The degree of persisence is deermined by he magniude of he sum. The effec of a shock on volailiy is said o be persisen over ime as his sum approaches 1. If rading volume is considered a proxy for informaion arrival, hen i is expeced ha θ > 0. If rading volume is serially correlaed, α and β will be small and saisically insignifican. The sum ( α + β) is smaller when rading volume is included han when i is excluded. All parameers of variance in equaion (4) can be esimaed using he Brend, Hall, Hall, and Husman (BHHH) algorihm, assuming general error disribuion (GED). B. Granger causaliy The Granger causaliy es (Granger, 1969) uses a bivariae equaion o es relaionships beween wo variables, x and y. The basic idea is ha if changes in x precede changes in y, hen x could be a cause of y, or vice versa. We used he following bivariae auoregressive model o es for causaliy among rading volume, sock reurns, and 34

3 Proceedings of he 2013 Inernaional Conference on Economics and Business Adminisraion volailiy of sock reurns: (4) m n x = + i 1 i i+ = = 1 + x a ax b y ε (5) m n y = + i 1 i i+ = = 1 + y c cy d x ε Suppose ha x and y are reurns and rading volume, respecively. In equaion (4), reurns are relaed o pas values of reurns as well as pas rading volume. In equaion (5), rading volume is relaed o pas values of reurns as well as pas rading volume. In equaion (4), if b coefficiens are saisically significan, hen including boh pas hisory of rading volume ( y ) and pas values of reurns ( x ) yields a beer forecas of reurns. Thus, we say ha volume causes reurns. If he F-es does no reec he null hypohesis ha b = 0 for all, hen he volume does no cause reurns. In equaion (5), if reurns cause volume, he coefficien will be non-zero. If boh b and d d are no zero, here is a feedback relaionship beween rading volume and reurns. To esimae vecor auoregressive (AR) model, he opimal lag lengh was obained using Akaike s informaion crierion (AIC) and Schwarz s Bayesian informaion crierion (SBIC) wih wo lag lenghs. I. SAMPLE DATA In he presen sudy, we used daily marke price index and rading volume daa from four Asian sock exchanges: Japan (NIKKEI 225), Hong Kong (Hang Seng Index, HSI), Korea (Korea Composie Sock Price Index, KOSPI), and China (Shanghai Sock Exchange Index, SSEI). We used daa from 2 January Sepember 2012 for all indexes excep for SSEI, for which we used daa from 23 Sepember 2005 o 31 December 2012; hese were obained from he Yunhap Informax Daa Cener. Daily index reurns and rading volume were calculaed in erms of percenage logarihmic change, based on he following formulae: r = ln( P P ) 100 (6 1 ) = ln( T T 1) 100 (7) where P is he daily closing index and volume. A. Descripive saisics T is he rading Tables 1 and 2 lis he descripive saisics for sock marke reurns and rading volume. Mean reurns were posiive for all markes excep ha of Japan. The measures for skewness indicaed ha he reurns were negaively skewed, excep for Hong Kong sock reurns. The kurosis was posiive for daily sock reurns and rading volume, and greaer han 3. This implies ha he disribuion of reurns and rading volume was no normally disribued. Applying he Jarque-Bera (J-B) es for normaliy reeced he null hypohesis of normaliy for reurns and rading volume. TABLE I. Summary saisics for daily reurns of sock markes Japan Hong Kong Korea China Mean Median Maximum Minimum Sd. Dev Skewness Kurosis Jarque-Bera [0.000]*** [0.000]*** [0.000]*** [0.000]*** Noes: Jarque-Bera (J-B) is he es saisic for he null hypohesis of normaliy in sample reurns disribuions. Numbers in brackes are p- values. Significance levels: ***1%, **5%, *10% TABLE II. Summary saisics for daily rading volume of sock markes (uni: million) Japan Hong Kong Korea China Mean Median Maximum Minimum Sd. Dev Skewness Kurosis Jarque-Bera [0.000]*** [0.000]*** [0.000]*** [0.000]*** Noe: See able 1. B. Uni roo ess We esed he saionariy of reurns and rading volume, for which he mos common es is he uni es. To es for a uni roo, we employed boh he augmened Dickey-Fuller (ADF) es and he Phillips-Perron (PP) es. Table 3 provides he resuls. The null hypohesis ha reurns and rading volume are nonsaionary was reeced a he 1% significance level, indicaing ha boh rading volume and reurns are saionary. TALBE III. Uni roo es for reurns and rading volume change daa 35

4 Proceedings of he 2013 Inernaional Conference on Economics and Business Adminisraion Reurns ADF PP Trading volume change Reurns Trading volume change Japan *** *** *** *** Hong Kong *** *** *** *** Korea *** *** *** *** China *** *** 41.98*** *** Noes: The criical values for he ADF and PP ess are and a he 1% significance level, respecively. ADF indicaes augmened Dickey-Fuller es, and PP indicaes Phillips-Perron es. Significance levels: ***1%, **5%, *10%.. EMPIRICAL RESULTS A Conemporaneous relaionships Table 4 presens he model of persisence of sock reurns when rading volume is included in boh he mean equaion and condiional variance for all sock reurns. The coefficiens of regressing reurns on rading volume were boh posiive and significan for he Korean and Chinese markes, negaive and significan for Hong Kong, and nonsignifican for Japan. When we incorporaed rading volume in he volailiy equaion, he coefficien was saisically significan for all sock markes. These resuls sugges ha conemporaneous volume significanly explains volailiy. We also found ha he GARCH effec sill remained for all marke reurns. This implies ha he volailiy of reurns is no oally explained by rading volume. This appears o be inconsisen wih he findings of Lamoureux and Lasrapes (1990), bu linking volailiy o rading volume does no exrac all informaion. We evaluaed he accuracy of model specificaion using Lung- Box (24) and ARCH (24) ess. Neiher es was significan a he 1% level, bu he esimaed model fi he daa very well. TABLE I. Conemporaneous relaionship beween daily rading volume and sock reurns µ λ ω α β θ Japan Hong Kong Korea China (0.0289) (0.0249) (0.0236)*** (0.0295)*** (0.0014) (0.0003)*** (0.0014)*** (0.0014)*** (0.0296)*** (0.0070)*** (0.0115)*** (0.0097)** (0.0171)*** (0.0091)*** (0.0140)*** (0.0101)*** (0.0240)*** (0.0077)*** (0.0158)*** (0.0108)*** (0.0015)*** (0.0057)*** (0.0027)*** (0.0006)*** α + β Q s (24) ARCH (24) [0.633] [0.680] [0.160] [0.148] [0.207] [0.189] [0.907] [0.891] LR Noes: Sandard errors are in parenheses and p-values are in brackes. The Lung-Box (24) saisic ess serial correlaions up o a 24 h order lag lengh in he squared sandardized reurns. The ARCH(24) saisic ess he ARCH effecs a 24 h order-lagged, squared residuals. LR indicaes log-likelihood. Significance levels: ***1%, **5%, *10% B. Domesic causal relaionships among rading volume, reurns and volailiy Table 5 presens he resuls of ess for domesic causal relaionships based on a bivariae model. Firs, he reurns daa show ha Granger causaliy affecs all of he markes. This implies ha reurns add significan predicive power for fuure rading volume in he presence of curren and pas rading volume. Second, a a 1% significance level, rading volume did no promp Granger-causaliy reurns in he Japan, Korea, or China markes. This confirms he difficuly of improving he predicabiliy of reurns by adding informaion flow abou rading volume, and is consisen wih he MDH (Clack, 1973), which predics no causal relaionship beween rading volume and reurns. However, rading volume did lead o Granger-causaliy reurns in he Hong Kong marke, in which reurns were influenced by rading volume and rading volume was influenced by reurns. This finding conradics he MDH and is consisen wih he SIAH (Copeland, 1976; Jennings e al., 1981). Trading volume has predicive power for fuure reurns. hypohesis R R TALBE. Causaliy relaionships among markes Japan Hong Kong Korea China F-saisic F-saisic F-saisic F-saisic (0.0673)* (0.8864) (0.0339)** (0.4142) (0.0216)** (0.0005)*** (0.0416)** (0.0404)** (0.9171) (0.8678) (0.2762) (0.0004)*** Noes: R=reurns; =rading volume change; =condiional volailiy filered by he GARCH model. Sandard errors are in parenheses. Significance levels: ***1%, **5%, *10% Third, beween rading volume and reurns volailiy, he F-saisics were highly significan, reecing he null hypohesis of no causaliy beween reurn volailiy (rading volume) and rading volume (reurns volailiy) in he Hong Kong and China sock markes. Tha is, rading volume helps predic reurns volailiy and vice versa. Trading volume conains informaion abou reurns indirecly 36

5 Proceedings of he 2013 Inernaional Conference on Economics and Business Adminisraion hrough he predicabiliy of reurns volailiy, bu no direcly via reurns iself. These resuls are in agreemen wih he findings of Clark (1973), Tauchen and Pis (1983), and Lee and Rui (2002). Reurns volailiy did no promp Granger-causaliy rading volume and he null hypohesis was reeced, bu he hypohesis ha rading volume does no lead o Granger-causaliy reurns volailiy was no reeced in he Japan and Korea markes. For rading volume, a a 1% significance level, Granger-causaliy reurns volailiy was reeced for he Korea sock marke. This implies ha rading volume helps predic reurns volailiy. Thus, in he Korea sock marke, here is unidirecional Granger causaliy from rading volume o reurn volailiy. However, he Japan sock marke shows he opposie causaliy: from reurn volailiy o rading volume. C. Cross-counry causal relaionships among rading volume, reurns and volailiy The resuls of Granger causaliy analyses among rading volume, reurns, and reurns volailiy for all markes sudied are presened in Tables 6 8. As can be derived from Table 6, Korea s rading volume helps predic he rading volume and volailiy in Japan as well as he volailiy in Hong Kong. Korea reurns do no lead o Granger causaliy of all oher variables excep he volume in Hong Kong and China. In addiion, Japan volume leads o Granger causaliy of rading volume in Korea as well as volailiy in Hong Kong and China. This implies ha Japan s volume influences he oher markes. However, Japan reurns do no lead o Granger causaliy of rading volume. China reurns do no have causal effecs on any oher markes. TABLE I. Cross-counry causal relaionship beween reurns and rading volume hypohesis F-saisic hypohesis F-saisic Panel A: Korea Japan period: 2/1/ /9/2012 JPR KO (0.5799) KO JPR (0.4090) KOR JP (0.8328) JP KOR (0.4534) Panel B: Korea, Hong Kong period: 2/1/ /9/2012 HKR KO (0.4211) KO HKR (0.3786) KOR HK (0.0352)** HK KOR (0.0003)*** Panel C: Korea China period: 23/9/ /9/2012 KOR (0.0003)*** KOR (0.3925) CIR KO (0.9138) KO CIR (0.8132) Panel D: Hong Kong Japan period: 2/1/ /9/2012 JPR HK (0.5058) HK JPR (0.0009)*** HKR JP (0.0920)* JP HKR (0.3386) Panel E: Hong Kong China period: 23/9/ /9/2012 HKR HKR (0.4482) CIR HK (0.7036) HK CIR (0.0238) Panel F: China Japan period: 23/9/ /9/2012 JPR (0.2521) JPR (0.2237) CIR JP (0.8817) JP CIR (0.2909) Noes: JPR=Japan reurns; JP=Japan volume changer; KOR=Korea reurns; KO=Korea volume change; HKR=Hong Kong reurns, HK=Hong Kong volume change; CIR=China reurns; =China volume change. Significance levels : ***1%, **5%, *10% TABLE II. Cross-counry causal relaionship beween rading volume and volailiy hypohesis F-saisic hypohesis F-saisic Panel A: Korea Japan period: 2/1/ /9/2012 JP KO (0.9320) KO JP (0.0087)*** KO JP (0.1764) JP KO (0.0007)*** Panel B: Korea Hong Kong period: 2/1/ /9/2012 HK KO (0.8217) KO HK (0.0956)* KO HK (0.5021) HK KO (0.9827) Panel C: Korea China period: 23/9/ /9/2012 KO (0.5587) KO (0.0350)** CI KO (0.9304) KO CI (0.1971) Panel D: Hong Kong Japan period: 2/1/ /9/2012 JP HK (0.0843)* HK JP (0.0340)** HK JP (0.4337) JP HK (0.0311)** Panel E: Hong Kong China period: 23/9/ /9/2012 CI HK (0.9160) HK CI (0.3214) HK (0.1301) HK (0.6181) Panel F: China Japan period: 23/9/ /9/2012 CI JP (0.8130) JP CI (0.0905)* JP (0.7290) JP (0.0272)** Noes: JP=Japan volume change; KO=Korea volume change; HK=Hong Kong volume change; = China volume change; =condiional volailiy filered by he GARCH model. Significance levels : ***1%, **5%, *10% Furhermore, as can be seen in Table 7, volailiy has virually no effec on any variable in any marke. The one excepion is ha volailiy in he Japan marke leads o Granger causaliy of rading volume in Hong Kong. As shown in Table 8, he only effecs of rading volume on he rading volume of anoher counry were beween Korea and Japan, where he volume of each led o Granger 37

6 Proceedings of he 2013 Inernaional Conference on Economics and Business Adminisraion causaliy of he oher. In sum, here are feedback relaionships beween Korea and Japan volume, beween Korea reurns and Hong Kong volume, and beween Hong Kong volume and Japan volailiy (Tables 6 8). Overall, however, here were few cross-counry ineracions. TABLE III. Cross-counry causal relaionship beween rading volume of each counry hypohesis F-saisic hypohesis F-saisic Panel A: Korea Japan period: 2/1/ /9/2012 KO HK JP (0.0078)*** JP KO (0.0050)*** Panel B: Korea Hong Kong period: 2/1/ /9/2012 KO (0.1654) KO HK (0.8201) Panel C: Korea China period: 23/9/ /9/2012 KO JP (0.9018) KO (0.8181) Panel D: Hong Kong Japan period: 2/1/ /9/2012 HK (0.4240) HK JP (0.7547) Panel E: Hong Kong China period: 23/9/ /9/2012 HK (0.6462) HK (0.6890) Panel F: China Japan period: 23/9/ /9/2012 HK (0.5754) HK (0.3546) Noes: JP=Japan volume change; KO=Korea volume change; HK=Hong Kong volume change; =China volume change. Significance levels : ***1%, **5%, *10% I. CONCLUSION We examined he dynamic relaionships beween reurns, rading volume, and volailiy for boh domesic and crosscounry markes. Our main goal was o deermine wheher rading volume as a proxy for informaion flow can be useful o improve he predicion of fuure reurns and reurn volailiy. GARCH analyses indicaed ha rading volume conribues some informaion o he reurns in Asian sock markes. However, GARCH effecs sill remained for all marke reurns. This implies ha he volailiy of reurns is no oally explained by rading volume. This evidence appears inconsisen wih he findings of Lamoureux and Lasrapes (1990). Our domesic Granger-causaliy resuls showed ha reurns led o Granger causaliy of he sock marke in all markes. In addiion, rading volume leads o Granger causaliy of he Hong Kong marke, and helps predic reurns volailiy in he Hong Kong and China markes (and vice versa). As o cross-counry effecs, he marke variables for Hong Kong have subsanial predicive power for financial marke variables in Korea and Japan. Korea volume helps predic Japan s rading volume and volailiy and Hong Kong volailiy. Japanese volume leads o Granger causaliy of Korea s volume and volailiy and Hong Kong and China volailiy. However, Chinese financial variables have a srong influence on oher marke variables. There are feedback relaionships beween Korea and Japan volume, beween Korea reurns and Hong Kong volume, and beween Hong Kong volume and Japan volailiy. ACKNOWLEDGEMENT This work was suppored by he Naional Research Foundaion of Korea Gran funded by he Korean Governmen (NRF B00008). REFERENCE [1] Alsubaie, A. and M. Naand, 2009, Trading volume, ime-varying condiional volailiy, and asymmeric volailiy spillover in he Saudi sock marke, Journal of Mulinaional Financial Managemen, ol. 19, No. 2, pp [2] Andresen, T. G., 1996, Reurn volailiy and rading volume: an informaion flow inerpreaion of sochasic olailiy, Journal of Finance, ol. 51, No. 1, pp [3] Avramov, D., T. Chordia and A. Goyal, 2006, The impac of rades on daily volailiy, Review of Financial Sudies, ol. 19, No. 1, pp [4] Bessemdbinder, H. and P. J. Seguin, 1992, Fuuresrading aciviy and sock price volailiy, Journal of Finance, ol. 47, No. 5, pp [5] Bollerslev, T., 1986, Generalized auoregressive condiional heeroskedasiciy, Journal of Economeric, ol. 31, No. 3, pp [6] Chen, G., M. Firh and O. M. Rui, 2001, The dynamic relaion beween sock reurns, rading volume, and volailiy, Financial Review, ol. 36, No. 6, pp [7] Chordia, T. and B. Swaminahan, 2000, Trading volume and cross-auocorrelaions in sock reurns, Journal of Finance, ol. 55, No. 2, pp [8] Clark, P. K., 1973, A subordinaed sochasic process model wih finie variance for speculaive prices, Economerica, ol. 41, No. 1, pp [9] Copeland, T. E., 1976, A model of asse rading under he assumpion of sequenial informaion arrival, Journal of Finance, ol. 31, No. 4, pp [10] Darra, A. F., S. Rahman and M. Zhong, 2003, Inraday rading volume and reurn volailiy of he DJIA socks: a noe, Journal of Banking & Finance, ol. 27, No. 10, pp [11] Engle, R. F. and. K. Ng, 1993, Measuring and 38

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