This study examines the relationship between returns and trading volume by using data on the Sensitive index of the Bombay Stock Exchange (BSE) from 1996 to 2006. Correlation analyses, GARCH (1,1) model and Granger causality tests are used to investigate contemporaneous and causal relationships between trading volume and return. A positive and insignificant contemporaneous correlation between return and trading volume was found. However, positive and significant impact of volume on return is detected in GARCH (1,1) model. In addition, GARCH (1,1) documents that the persistence of variance over time partly declines if one includes trading volume as a proxy for information arrivals in the equation of conditional volatility. It also shows the negative impact of volume on conditional volatility because of asymmetry that is observed in significant Jarque-Bera. Furthermore, no causality exists between volume and returns indicating that volume doesn't contain predictive power for the direction of price changes.
AndersenT. G. (1996), “Return Volatility and Trading Volume: An Information Flow Interpretation of Stochastic Volatility,”Journal of Finance, Vol. 51, pp. 169–204.
2.
Bhanupant (2001), “Testing Dynamic Relationship between Returns and Trading Volume on the National Stock Exchange,” Downloaded from http://www.utiicm.com/bhanupant.html on March 27, 2006.
3.
BlumeL.EasleyD.HaraM. O. (1994), “Market Statistics and Technical Analysis: The Role of Volume,”The Journal of Finance, Vol. 49 (1), pp. 153–181.
4.
BollerslevT. (1986), “Generalized Autoregressive Conditional Heteroscedasticity,”Journal of Econometrics, Vol. 31, pp. 307–327.
5.
BrailsfordT. J. (1996), “The Empirical Relationship between Trading Volume, Returns and Volatility,”Journal of Accounting and Finance, Vol. 35, pp. 89–111.
6.
CinerCetin, (2002), “The Stock Price-Volume Linkage on the Toronto Stock Exchange: Before and After Automation,”Review of Quantitative Finance and Accounting, Vol. 19 (4), pp. 335–349.
7.
CinerCetin, (2003), “The Dynamic Relationship between Stock Prices and Trading Volume: An Examination of Call Markets,” [Available from http://207.36.165.114/Denver/Papers/callmarkets.pdf on May 26, 2006].
8.
ChenFirth G.M.RuiO. (2001), “The Dynamic Relation between Stock Returns, Trading Volume and Volatility,”The Financial Review, Vol. 36 (3), pp. 153–174.
9.
ComiskeyE. E.WalkingR. A.WeeksM. A. (1984), “Dispersion of Expectations and Trading Volume,”Journal of Business Finance and Accounting, Vol. 14 (2), pp. 229–239.
10.
EngleR. (1982), “Autoregressive Conditional Heteroskedasticity with Estimates of United Kingdom Inflation,”Econometrica, Vol. 50, pp. 987–1008.
11.
EppsT. W.EppsM. L. (1976), “The Stochastic Dependence of Security Price Changes and Transaction Volumes: Implications for the Mixture of Distributions Hypothesis,”Econometrica, Vol. 44 (2), pp. 305–321.
12.
FamaE. F. (1965), “The Behavior of Stock-Market Prices,”The Journal of Business, Vol. 38, pp. 34–105.
13.
GallantRossiTauchen (1992), “Stock Prices and Volume,”The Review of Financial Studies, Vol. 5 (2), pp. 199–242.
14.
GalloG. M.PaciniB. (2000), “The Effects of Trading Activity on Market Activity,”European Journal of Finance, Vol. 6, pp. 163–175.
15.
GodfreyGranger C. W. J.MorgensternO. (1964), “The Random Walk Hypothesis of Stock Market Behavior,”Kyklos, Vol. 17, 1–30.
16.
GrangerC. W. JMorgernsternO. (1963), “Spectral Analysis of New York Stock Market Prices,”Kyklos, Vol. 16, pp. 1–27.
17.
GriffinJ. F., (2004), “Stock Market Trading and Market Conditions,” Working Paper, [Available from http://www.nber.org on June 16, 2006].
18.
Hanna, (1978), “Security Price Changes and Transaction Volumes: Additional Evidence,”The American Economic Review, Vol. 68 (4), pp. 692–695.
19.
HarrisL.GurelE. (1986), “Cross-Security Tests of Mixture of Distribution Hypothesis,”The Journal of Financial and Quantitative Analysis, Vol. 21 (1), pp. 39–46.
20.
HiemstraC.Jones (1994), “Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation,”The Journal of Finance, Vol. 49 (5), pp. 639–1664.
21.
JoherHuson, (2005), “The Relationship between Trading Volume, Volatility and Stock Market Returns: A Test of Mixed Distribution Hypothesis for A Pre and Post Crises on Kuala Lumpur Stock Exchange,”Investment Management and Financial Innovations, Vol. 3, pp. 146–158.
22.
JainP. C.JohG. (1986), “The Dependence between Hourly Prices and Trading Volume,”The Journal of Financial and Quantitative Analysis, Vol. 23 (3), pp. 269–283.
23.
JamesEdmister (1983), “The Relation between Common Stock Returns, Trading Activity and Market Value,”The Journal of Finance, Vol. 38 (4), pp. 1075–1086.
24.
KamathR. R., (1998), “Return Distribution and The Day-of-the-Week Effects in the Stock Exchange of Thailand,”Journal of Economics and Finance, Vol. 22, pp. 97–106.
25.
KarpoffJ.M., (1987), “The Relation between Price Changes and Trading Volume: A Survey,”The Journal of Financial and Quantitative Analysis, Vol. 22 (1), pp. 109–126.
26.
KarpoffJ. M., (1988), “Costly Short Sales and the Correlation of Returns with Volume,”The Journal of Financial Research, Vol. 11 (3), pp. 173–188.
27.
KimD.KonS. J. (1994), “Alternative Models for the Conditional Heteroescdasticity of Stock Returns,”Journal of Business, Vol. 67, pp. 563–598.
28.
KocagilA. E.ShachmuroveY. (1998), “Return-Volume Dynamics in Futures Markets,”The Journal of Futures Market, Vol. 18 (4), pp. 399–426.
29.
LamoureuxC. G.LastrapesW. D., (1990), “Heteroskedasticity in Stock Return Data: Volume Versus GARCH Effects,”The Journal of Finance, Vol. 45 (1), pp. 221–229.
30.
LeeB. S.RuiO. M. (2000), “Does Trading Volume Contain Information to Predict Stock Returns? Evidence from China's Stock Markets,”The Review of Quantitative Finance and Accounting, Vol. 14 (4), pp. 341–360.
31.
LeeB. S.RuiO. M., (2002), “The Dynamic Relationship between Stock Returns and Trading Volume: Domestic and Cross-Country Evidence,”Journal of Banking and Finance, Vol. 26 (1), pp. 51–78.
MoolmanH. C., (2004), “An Asymmetric Econometric Model of the South African Stock Market,” Ph.D Thesis submitted to Faculty of Economics and Management Science at University of Pretoria, 1–195.
34.
MorganI. G., (1976), “Stock Prices and Heteroskedasticity,”Journal of Business, Vol. 49, pp. 496–508.
35.
NguyenDiagler, (2005), “A Return-Volatility-Volume Analysis of Futures Contracts,” [Available from http://www.fma.org/ on May 18, 2006].
36.
OmranM. F.McKenzieE. (2000), “Heteroskedasticity in Stock Returns Data Revisited: Volume versus GARCH effects,Applied Financial Economics, Vol. 10, pp. 553–560.
37.
OtavioR.BernardusF. N., (2006), “The Empirical Relationship between Stock Returns, Return Volatility and Trading Volume in the Brazilian Stock Market,” SSRN Working Paper, [Available from http://www.ssrn.com on March 17, 2006].
38.
ReddyS. Y. (1997), “Efficiency of Indian Stock Markets: An Empirical Analysis of Weak-form EMH of the BSE,” UTI Indian Capital Market Conference December, 91–115.
39.
RichardsonSefcikThompson, (1987), “A Test of Dividend Irrelevance Using Volume Reaction to a Change in Dividend Policy,”Journal of Financial Economics, Vol. 17, pp. 313–333.
40.
RogalskiR. J., (1978), “The Dependence of Prices and Volume,”The Review of Economics and Statistics, Vol. 60 (2), pp. 268–274.
41.
SchwertG. W. (1990), “Stock Volatility and the Crash of 87,”Review of Financial Studies, Vol. 3, pp. 77–102.
42.
SharmaJ. L.MougoueM.KamathR. (1996) “Heteroscedasticity in Stock Market Indicator Return Data: Volume Versus GARCH Effects”,Applied Financial Economics, Vol. 6, pp. 337–342.
43.
SmirlockM.StarksL. (1988), “An Empirical Analysis of the Stock Price-Volume Relationship,”The Journal of Financial Research, Vol. 8, pp. 31–41.
StevensonA. R.BearM. R. (1970), “Commodity Futures: Trends or Random Walks?”, The Journal of Finance, Vol. 25, pp. 65–81.
46.
SuominenM. (2001), “Trading Volume and Information Revelation in Stock Markets,”The Journal of Financial and Quantitative Analysis, Vol. 36 (4), pp. 546–565.
