ZENITH International Journal of Multidisciplinary Research Vol.1 Issue 6, October 2011, ISSN 2231 5780
TESTING OF RELATIONSHIP BETWEEN STOCK RETURN AND TRADING VOLUME IN INDIA
RAVI KANT*
*Doctoral Student, Department of Commerce, Kurukshetra University, Kurukshetra-136119, Haryana, India.
ABSTRACT
The linkage between stock prices and trading volume has been subjected to extensive research worldwide. The issue is also gaining importance to India especially in post liberalisation period. In this context this article attempts to empirically examine the relationship between stock returns and trading volume in India using monthly data time series over a nine year period from January 2002 to December 2010 for three hundred forty seven Indian stocks. The study employed the three steps in the first step descriptive study, in the second step augmented dickey-fuller unit root test for checking stationery and in the third step granger causality tests for testing the causality between stock return and trading volume.Granger causality test find that there is high degree causality between stock return and trading volume in Indian stock market because out of three hundred forty seven stocks, 66% stocks indicate that return cause volume, 3.3% stocks indicate that volume cause return, 3.7% stocks indicate bi-directional causation and the remaining27% shows no causation at all.
KEYWORDS: Empirically,Granger Causality Test, Stock Return, Trading Volume, Time Series. ______
INTRODUCTION
There are many studies conducted on the relationship between trading volume and stock return. Such studies can provide insight into the structure of the financial market. These studies have been carried out on the stock market of countries other than India on the relationship between trading volume and stock return relationship. In these studies, a positive relationship between trading volume and stock return has been documented. This positive relationship has partially been attributed to the asymmetric relationship between these two variables. It is generally believed that the relationship between stock return and trading volume can provide an insight into the structure of capital market. The main objectives of study to ascertain the relationship between the trading volume and stock return changes in the Indian Stock Exchanges. Second the correlation between return changes and trading volume as well as magnitude of return changes and trading volume. The role of information on pricing of stock is an issue heavily discussed in the areas of finance, economics, and accounting. Generally it is known that pricing react to the arrival of new information. Investors in the stock markets frequently revise their expected prices of stocks depending on the flow of information. Possible disagreement to informational events can also lead to increased trading. Trading volumes can increase even if investors interpret the information
identically but they have divergent prior expectations. Yet it is not clear what is the www.zenithresearch.org.in information reflected by volume data. The effects of the institutional and regulatory design of the market-spot and futures—on trading volume are also not well understood. This study
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ZENITH International Journal of Multidisciplinary Research Vol.1 Issue 6, October 2011, ISSN 2231 5780 analyses the impact of stock market liberalization on emerging return volatility. A liberalization period is constructed to capture all identified market openings for each market.
The stock market is a really nice place to invest your hard-earned money. Provided you know all the ropes. The Stock Market is no place for the tender-hearted and the weak- kneed. The Stock Market is a Great Leveller, in that it lifts you up one day and dumps you on the mat, the very next day. This is true in the case of the Indian Stock Market, the undisputed leader of the Asian pack. Here the foreign institutions hold all the trump cards. The domestic institutions and the mutual funds do play important roles but they are in no position to change the trend of the market on a given day. The current study contributes to the existing literature in volume changes and returns changes relationship by using traded quantity and calculated return from monthly adjusted closing prices in the Indian industries. The causal relationship is investigated between volume and return volatility by using granger causality test.
OBJECTIVES OF STUDY
This study is undertaken with the following objectives:
1 To study the correlation between return changes and trading volume as well as magnitude of return changes and trading volume.
2 To study that can trading act as a barometer for the Indian economy.
3 To test the causality relationship between the trading volume and stock return through granger causality test.
THE NEED OF THE STUDY
There exists a considerable amount of evidence both ‗for‘ and ‗against various level of efficiency for developed capital market. However, the capital market of the developing world such as that of India has been less subjected to efficiency research. Also these studies have employed relatively short time period. Therefore, further investigations on individual stock return data and over longer periods would provide more conclusive evidence. The knowledge of this relationship between trading volume and stock return in stock market can prove useful for investors. By properly timing their buy and sell decisions, they can enhance their adjusted profit, altering the time of routinely scheduled transaction in the light of trading volume changes can enhancing one‘s return on investment. The proposed study provides a useful insight into the behaviour of trading volume and return changes in the Indian capital market. It may help the professional and institutional investor to better meet the expectations of their client at large.
REVIEW OF LITERATURE
During the last decades a number of interesting studies have sought to explain the empirical relationship between trading volume and stock returns.
We argue that the increase in trading volume and return volatility may be attributed to index arbitrage transactions as derivative markets provide more routes for index arbitrageurs to trade. Volume and Seemingly Emotional Stock Market Behaviour that various information www.zenithresearch.org.in types and rational learning methods have shown that heterogeneous belief changes in a
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ZENITH International Journal of Multidisciplinary Research Vol.1 Issue 6, October 2011, ISSN 2231 5780 rational expectation model can explain many empirical findings in stock markets, such as momentum, contrarians, and technical trading. The methods have also shown that momentum and price movements can coexistin an asset market with only rational agents. The purpose of this paper is to provide a rational economic theory to explain these phenomena. Results of a dynamic programming model with heterogeneous beliefs show that the dynamic interactions between information diffusion and belief changes create continuation and reversals. The duration and magnitude of momentum and price movements are associated with trading volume. Therefore, rational investors should incorporate price andvolume information in their trading decisions.
Lam, Li and Wong (1990) made a study to measure Price changes and trading volume relationship in the Hong Kong stock market that Studies on the relationship between price changes and trading volume can provide insight into the structure of the financial market. In this paper, we will study the above topic and concentrate on the stock market of Hong Kong. The correlation between price changes and trading volume as well as that between the magnitude of price changes and trading volume will be examined. We will also check the asymmetry of the price changes and volume relationship. Moreover, we will investigate the relationship between the variance of return and trading volume. Finally, the Granger causality test of price changes and volume will be performed.
Mittal (1995) has documented in his article, new finding on price changes and trading volume relationship in the Indian stock market that a positive relationship may be observed between stock price changes and trading volume, Lagged relationship between these two variables my also be possible. The study is based on two daily prices indices published by BSE, BSE sensitive index, and BSE national index, which are assumed to be market informer. It is evident that stock price changes and trading volume are not significantly correlated.
Henry (1999) analysed in his research paper Stock Market Liberalization, Economic Reform, and Emerging Market Equity Prices A stock market liberalization is a decision by a country‘s government to allow foreigners to purchase shares in that country‘s stock market. This result is consistent with the prediction of standard international asset pricing models that stock market liberalization may reduce the liberalizing country‘scost of equity capital by allowing for risk sharing between domestic and foreign agents.
Bhanupant (2001) examined in this study,Testing Dynamic Relationship between Returns and Trading Volume on the National Stock Exchange that the dynamic relationship between stock index returns andtrading volume using the linear and non-linear Granger non-
causality hypothesis test onthe National Stock Exchange (NSE) data. Widely used linear
Granger non-causality testis used to investigate the linear relationship while the non-linear Granger causality isinvestigated using modified Baek and Brock test proposed by Hiemstra and Jones (1994)for the daily returns on S&P CNX Nifty and the total trading volume at NSE.
Bhatiacharya&Mukkherjee (2003) examine in their paper, Causal Relationship between Stock Market and Exchange Rate, Foreign Exchange Reserve and Value of Trade Balance: A case Study for India that the nature of the causal relationship between stock prices
and macroeconomic aggregates in the foreign sector in India. By applying the techniques of www.zenithresearch.org.in unit–root tests, co- integration and the long–run Granger non–causality test recently proposed by Toda and Yamamoto (1995), we test the causal relationships between the BSE Sensitive
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Index and the three macroeconomic variables, viz., exchange rate, foreign exchange reserves and value of trade balance using monthly data for the period 1990-91 to 2000-01. The results suggest that there is no causal linkage between stock prices and the three variables under consideration.
Abounoori andMotameni (2007) investigated in this study, Test of Leverage Effect in Tehran Stock Exchange that the effect of stock volatility is examined in Tehran Stock Market (TSE) using the leverage effect theory. According to the theory, the return and variance have negative relationship. The effect has been tested using E-GARCH model and daily time series data during 1992-2006. The results do not reject the leverage effect in TSE. Also, the impact of good and bad news on volatility has not been symmetric.
Shantha and Shnmugham (2007) investigated in his paper, Behaviour of stock returns to the Corporate Bonus Issue Announcements: An Event Study that the Indian corporate sector has witnessed restructuring initiatives which created an impact in the behaviour of the stock returns. This subject has captured the attention of the researchers and practitioners in the areas of corporate finance. This study examines the stock price reaction of the companies‘ constitution the S&P CNX 500 around the bonus issue announcement dates. The period of study is Jan 2000 to Dec 2005. Event study methodology was employed on the sample companies. The sample was decided into sub samples based on the industry type, level o0f EPS and P/E ratios. The results proved that the markets react positively to the bonus issue announcements.
Ashraf and Ahmed (2008) found in their research, causality and volatility in the firm level stock returns and volume in India: evidence from national stock exchange that co- movement in stock return and volume changes using daily NSE data for 21 listed firms from 1996 to 2005. It is observed that the direction of causality between stock return and volume change very over different periods and across firms. Generally there are causal relationships between volume and price over the full period. Once we take the three sub-periods the relationship starts to weaken over the sub-periods for most of the stocks.
Mahajan and Singh (2008) found in their research, trading volume and return volatility dynamics in Indian stock market that the emergence of informational efficient financial markets is an important facet of any country‘s economic modernization with far- reaching implications for its macroeconomic stability and performance, trading volume conveys crucial information on future stock prices. Which are of interest to all market participants, this paper examines the pattern of information flow between trading volume and return volatility (squared value of returns) using daily data of closing prices and volume of
nifty index for the period from July, 2001 to march, 2006.
RESEARCH METHODOLOGY
İn order to observe the relationship between stock return and trading volume ,the methodology was used in three step
1 Descriptive study
2 ADF test for checking stationarity of time series data. www.zenithresearch.org.in 3 Granger causality test
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FRIST STEP
Descriptive statistics:-This displays various summary statistics for the series. It contains entries for histograms, basic statistics, and statistics by classification.
This displays the frequency distribution of your series in a histogram. The histogram divides the series range (the distance between the maximum and minimum values) into a number of equal length intervals or displays a count of the number of observations that fall into each bin.
A complement of standard descriptive statistics is displayed along with the histogram. All of the statistics are calculated using the observations in the current sample.
1. Mean is the average value of the series, obtained by adding up the series and dividing by the number of observations.
2. Median is the middle value (or average of the two middle values) of the series when the values are ordered from the smallest to the largest. The median is a robust measure of the centres of the distribution that is less sensitive to outliers than the mean.
3. Max and Min are the maximum and minimum values of the series in the current sample.
4. Std. Dev. (standard deviation) is a measure of dispersion or spread in the series. The standard deviation is given by:
Where the number of observations in the current is sample and is the mean of the series.
5. Skewness is a measure of asymmetry of the distribution of the series around its mean. Skewness is computed as:
Where is an estimator for the standard deviation that is based on the biased estimator for the variance . The skewness of a symmetric distribution, such as the normal distribution, is zero. Positive skewness means that the distribution has a long right tail and negative skewness implies that the distribution has a long left tail.
6. Kurtosis measures the peakiness or flatness of the distribution of the series.
Kurtosis is computed as www.zenithresearch.org.in
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where is again based on the biased estimator for the variance. The kurtosis of the normal distribution is 3. If the kurtosis exceeds 3, the distribution is peaked (leptokurtic) relative to the normal; if the kurtosis is less than 3, the distribution is flat (platykurtic) relative to the normal.
7. Jarque-Bera is a test statistic for testing whether the series is normally distributed. The test statistic measures the difference of the skewness and kurtosis of the series with those from the normal distribution. The statistic is computed as:
where is the skewness, and is the kurtosis.
Under the null hypothesis of a normal distribution, the Jarque-Bera statistic is distributed as with 2 degrees of freedom. The reported Probability is the probability that a Jarque-Bera statistic exceeds (in absolute value) the observed value under the null hypothesis-a small probability value leads to the rejection of the null hypothesis of a normal distribution. We reject the hypothesis of normal distribution at the 5% level but not at the 1% significance level.
SECOND STEP
DICKEY-FULLER UNIT ROOT TEST (STATIONARY TEST)
Let TQ = Yt, the DF Unit Root Test are based on the following three regression equation forms:
1. without Constant and Trend
2. with Constant
3. with Constant and Trend
Where is the variable of interest, t is the time index, ρ is a coefficient, and ut is the error term. A unit root is present if ρ = 1.α is a constant, β the coefficient on time trend, µ is the error term,this model can be estimated and testing for a unit root is equivalent to testing δ = 0 (where δ = ρ − 1).∇ is the first difference operator.Each version of the test has its own critical value which depends on the size of the sample. In each case, the null hypothesis is that there is a unit root, δ = 0. The tests have low statistical power in that they often cannot distinguish between true unit-root processes (δ = 0) and near unit-root processes (δ is close to zero). This is called the "near observation equivalence" problem.Since the test is done over the residual term rather than raw data, it is not possible to use standard t-distribution to www.zenithresearch.org.in
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ZENITH International Journal of Multidisciplinary Research Vol.1 Issue 6, October 2011, ISSN 2231 5780 provide critical values. Therefore this statistic τ has a specific distribution simply known as the Dickey–Fuller table.
THE HYPOTHESIS IS
Decision rule:
If t* > ADF critical value, ==> not reject null hypothesis, i.e., unit root exists.
If t* < ADF critical value, ==> reject null hypothesis, i.e., unit root does not exist.
Note: - TQ denotes Traded Quantity, RTN denotes Stock Return.
THIRD STEP
GRANGER CAUSALITY TEST
If we want to know whether
"TQ" causes "RTN" or "RTN" causes "TQ", or bilateral causes,
"Lags to include" is "2",
HYPOTHESES ARE
The null hypotheses of the Granger-Causality test are:
H0: X =/=> Y (X does not granger-cause Y)
H1: X ==> Y (X does Granger-cause Y)
The F-test is: F(r, n-m-k) = [(ESSr -ESSu)/r]/[ESSu/(n-m-k)] and ESS is the residual sum of squares.
If, F-statistics are large and the probability values are all close to 0. It means that the variables are mutually Granger cause to each other.
Correlation does not necessarily imply causation in any meaningful sense of that word. The econometric graveyard is full of magnificent correlations, which are simply spurious or meaningless.
The Granger (1969) approach to the question of whether causes is to see how much of the current can be explained by past values of and then to see whether adding lagged values of can improve the explanation. is said to be Granger-caused by if helps in the prediction of , or equivalently if the coefficients on the lagged 's are statistically significant. Note that two-way causation is frequently the case; Granger causes and Granger causes . www.zenithresearch.org.in
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It is important to note that the statement " Granger causes " does not imply that is the effect or the result of . Granger causality measures precedence and information content but does not by itself indicate causality in the more common use of the term.
When you select the Granger Causality view, you will first see a dialog box asking for the number of lags to use in the test regressions. In general, it is better to use more rather than fewer lags, since the theory is couched in terms of the relevance of all past information. You should pick a lag length, , that corresponds to reasonable beliefs about the longest time over which one of the variables could help predict the other.
E-Views run bivariate regressions of the form:
For all possible pairs of series in the group,the reported F-statistics are the Wald statistics for the joint hypothesis:
For each equation,the null hypothesis is that does not Granger-cause in the first regression and that doesnot Granger-cause in the second regression.
Note: The Granger Causality Test for some variables is very sensitive to the selected number of lags in the analysis. Students should be careful to choose the reasonable lag lengths. For examples, for the monthly data, the reasonable lag terms can be range from 1 to 12 or 24, etc.; for the quarterly data, the reasonable lag terms can be range from 1 to 4, 8, 12, etc., and for the annual data, the reasonable lag terms should be less. And the reasonable lag- lengths should be determined by the significant change of the F-value.
DATA AND SAMPLE
The sample of this study comprises three hundred forty seven listed companies on BSE of ten 10 industries which is the major stock exchange of India. These industries are Automobile, Cotton Textile, Transport Services, Electricity, Electronics, Metal, Non-metal, Real Estate, Mining and Chemical. The data period is Jan. 2002 to Dec. 2010.
ANALYSIS
DESCRIPTIVE STATISTICS- TRADING VOLUME AND STOCK RETURN
The study presents descriptive statistics for the returns calculated on return and trading volume for the 108 and 107 observations have been calculated from the closing prices extracted from prowess data base of CMIE ltd. The statistics have been calculated by using the E-views and SPSS software.
The mean, median, maximum, minimum, std. dev., skewness, kurtosis, Jarque-Bera, probability, sum and sum sq. dev. simple correlation are calculated to do the preliminary investigation. The results are presented in this study by Giving N=108 and 107 observations. www.zenithresearch.org.in We use the adjusted closing prices to calculate the return series . We define returns as
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100* , where is the closing price on day t. To represent trading volume, N=No. of observation, we use the aggregate monthly number of shares traded for the component stocks of the BSE. We present the statistical characteristics of the empirical distribution of return on the Indian stock market and trading volume in order to determine if the data for the stock market exhibit characteristics consistent with the predictions of standard mixture of distributions models. It can be observed from study that the average value of the series in case of volume is positive and in case of return% is also positive. Its mean that the return frequency in the series positive more than positive. The average of two middle values of the series is when the values are ordered from the smallest to the largest. The volume remains positive and return% also positive. Both of the maximum and minimum values of the series are positive in case of traded quantity and in case of return minimum value are negative and maximum is positive it means that the high point in volume and returns are larger than other and vice versa. During measuring the variance round the mean the s.d. of volume is positive and return% is also positive. Skewness is a measure of asymmetry of thedistribution of the series around its mean. The volume distributionround the mean has long right tail and return has also long right tail.
DICKEY-FULLER UNIT ROOT TEST (STATIONARY TEST) - TRADING VOLUME AND STOCK RETURN
All data used in the studyis stationary for both trading volume and stock return.ADF test has been employed for checking the stationarity of time series data. The lag length, for the ADF tests was chosen so as to making data more suitable for testing granger causality testlags to include iszero for ADF and two for causality tests. The critical value for ADF unit root tests at 1%, 5% and 10% levels are -2.586960, -1.943882 and -1.614731(without trend).
GRANGER CAUSALITY TEST- TRADING VOLUME AND STOCK RETURN
As the below table shows, the results of granger causality test, F-test at firm level are reported in the table. Out of 347 stock, 229 stocks indicate that return cause volume, 11 stocks indicate that volume cause return, 13 stocks indicate bi-directional causation and the remaining 94 shows no causation at all. As you can see, F-statistics are large and probability values are all close to 0 except for some companies. It means the variables are mutually granger cause to each other accepts some companies whose F-statistics are not large and probability values are not all close to 0.
Empirical result ofpair wise granger causality tests at firm level for the period of Jan. 2000 to Dec. 2010.
Return cause volume Volume cause return Bi-directional No causation causation
Industry F- Industry F- Industry F- Industry F- stat. stat. stat. stat.
HPCTL 18.73 ALL 4.96 ACHL 1.50
3.99* www.zenithresearch.org.in EML 13.66 SKNL 2.67 BASF 10.24 HHML .38
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6.53*
HML 22.89 SSML 2.37 CASTROL 14.51 MML 1.95
3.89*
KEL 15.75 BSAL 1.89 COLGATE 4.32 TATAM 1.27
2.73*
KMCL 7.40 CTIL 3.03 DKL 7.04 ARL 1.76
3.61*
LML 9.37 OAL 5.96 PIL 8.03 ANPL 0.03
3.34*
MSL 18.68 REL 4.82 SSML 3.02 BPCL 0.96
4.02*
MAL 4.57 RIL 7.04 WYL 12.55 CIPLA .51
3.07*
SMLIL 3.92 CCOIL 3.05 NLCL 25.85 CCIL .25
7.52*
STIL 8.25 GEST 5.59 MECL 27.01 DABUR .45
4.26*
SIL 4.94 DRREDDY 2.82 KCL 3.68 DFPC 1.88
2.58*
TVS 10.11 MCL 3.78 GLAXOPL 1.79
3.33*
APL 15.07 SCL 25.37 GODREG 0.86
5.28*
APCL 13.29 HLL 0.84
ATL 8.27 IPCA .31
AOL 18.95 MAX .13
CADILA 14.11 RANBAXYL 1.75 www.zenithresearch.org.in
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ZENITH International Journal of Multidisciplinary Research Vol.1 Issue 6, October 2011, ISSN 2231 5780
CEAT 6.95 SPIL 1.89
CFCL 5.76 TCL 1.83
CPCL 15.20 DCM 2.05
DCW 10.09 HSWM .48
EPIC 6.52 RSWM 1.87
ESSAR 43.04 RSWM 1.87
EIL 18.12 SSIL 1.18
GHCL 12.01 SVSML 1.99
GPL 16.73 BFUL 1.96
GBL 5.52 NBVL 1.61
GNVF 4.43 NTPC 1.53
GSFC 6.42 BEL 1.69
HOCL 11.24 NCPL .50
HPCL 4.91 GIEL 1.82
JBCL 3.51 GCL 1.20
JAINIS 2.64 HFCL 1.97
KOPRAN 10.67 HRL .11
LUPIN 15.02 JCT .07
MCFL 24.03 LMEL 1.32
MRPL 8.05 OCL 1.53
MLL 7.51 VIL 2.23
NFCL 11.88 VIL 2.26
RTN 5.73 AFL .39
NIL 5.76 ACL 1.52
RCFL 21.96 CCOM 2.07
RMTL 7.10 DNIL 1.68 www.zenithresearch.org.in NSBL 6.92 FACL .16
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ZENITH International Journal of Multidisciplinary Research Vol.1 Issue 6, October 2011, ISSN 2231 5780
NTPL 15.11 GSTL .86
UFLEX 6.13 HIL 1.30
VDML 3.15 HTWL .08
WL 10.65 JAIC 1.09
ADEL 7.79 JSL 1.04
NAIL 7.83 KPTL .38
ASML 2.94 LIL 1.84
NASIL 16.12 MMFL 1.67
ARVIND 13.96 MSL 1.08
ASHIMA 2.47 NACL 1.06
NBSL 5.26 OSISL .54
EKNI 15.53 PIL 1.31
EIEL 10.52 RSPL .09
GTL 5.80 RMTL 1.81
GFL 13.26 SHETRON .46
NICIL 8.10 STL 2.20
QKGD 12.06 TATASTEEL .13
NLMC 10.90 TIOL .72
MCSM 10.63 ZENITH .03
MOL 8.63 GMDC 2.21
NMTL 7.16 GNRE .53
NNEL 8.37 ONGC 1.01
NSML 6.49 SESAG .29
PUDIL 3.14 AMBUJACL .30
SSML 9.91 ACL 1.00
SRSL 14.37 AGIL .30 www.zenithresearch.org.in STIL 3.03 CUL 1.34
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ZENITH International Journal of Multidisciplinary Research Vol.1 Issue 6, October 2011, ISSN 2231 5780
NSIL 2.36 ICL 2.03
SLST 5.80 RAMCOIL 1.48
SSML 4.60 SDCC 1.92
TT 8.73 SSGL .01
VPL 5.73 SDL 1.21
VTL 2.72 TITAN 1.39
CESL 3.86 AFHL .75
GIPCL 18.26 HBED .14
RELIANIN 3.20 PENINSULA .13
NADC 18.38 RDL .21
AGC 2.39 UNITECH .37
ATL 13.63 BTL 2.05
APLAB 4.18 DCOIL 2.05
AEL 4.82 ESPLL 2.04
AL 9.18 SICALLL 1.60
BPL 8.84 PHL .02
CEL 4.30 RIL .52
CITL 4.16 GTNIL 1.16
COSMOF 27.68 NLTM 1.67
DSSL 11.50 ACC 2.07
HAIL 2.78 ASCL .25
ITI 14.22 VIL 1.49
LECS 10.61 LHCL .32
MEL 22.25 Return cause volume Return cause Return cause volume volume
MOSERBAER 4.09 Industry F- Industry F- Industry F-
stat. stat. stat. www.zenithresearch.org.in MROTEK 6.32 SAL 18.45 GIL 4.87 PCL 22.39
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NELCO 9.61 SBCL 2.47 GNL 2.49 RCL 6.03
NPCS 8.55 SAIL 16.12 GSCL 14.09 RCL 12.31
PCL 5.42 STERLINGTL 5.20 WCL 22.74 SBTIL 14.64
RVL 8.74 SISC 6.30 WISL 5.56 SGSI 7.20
SPEL 11.10 TATAML 8.80 CMRL 19.97 SCL 6.09
SCL 10.94 TATASI 14.67 HCIL 6.27 SCL 4.23
SHARPIL 7.08 TAYOR 28.89 HOEC 9.00 SSIL 3.84
STL 16.86 TCIL 3.48 INSILCO 15.03 SCL 4.01
SIEMENS 4.83 USHAM 2.90 OCCL 13.86 SRDG 13.69
SNSL 6.50 UGSL 7.65 SETL 40.38 SDE 7.24
STGL 10.46 VBC 4.85 MCL 9.60 TRIVENIG 13.08
VXLI 10.24 MIL 5.74 NCIL 10.70 VGL 12.93
VCL 11.32 METALIL 4.22 OCLI 11.05 VISAKAIL 4.83
NXOIL 7.22 MIEL 25.29 OCLI 9.77 ANSALBL 5.22
ZCL 5.45 MUKAND 3.71 OPIL 2.53 AHCL 6.88
ZESSL 15.79 NSAI 14.31 PPL 7.56 APIL 7.04
BSL 7.35 NILE 8.95 AIGL 4.11 DSKD 5.13
BSIL 4.56 OCTL 14.86 NBCL 2.56 ABCINDIA 15.30
BIL 6.60 OEL 2.77 BIRLAC 3.21 BDEL 4.29
NECL 10.00 PGFOIL 43.58 BGWL 7.68 CSL 13.56
ESIL 3.14 PSL 24.12 CDL 5.13 GOSL 9.72
EGCL 3.84 RGWL 8.83 DECCAN 7.71 GATI 5.61
GIL 9.63 RCL 11.77 EIL 7.52 JAGSONA 9.73
GPL 5.56 RSAL 24.37 GIL 16.15 MLL 3.94
HAIL 16.49 SIL 11.06 HCIL 6.10 PILL 9.61
HZL 7.77 JINDALSAW 6.51 IFGLR 5.26 SKSL 6.78 www.zenithresearch.org.in IFL 4.15 JINDALSTEE 2.67 IHPCL 54.74 SCOIL 12.06
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IPCL 2.88 KALYANISTE 17.14 JKCL 7.68 SSLL 6.02
ISPAT 8.25 KFIL 6.00 KCSIL 21.53 SPICEJET 4.76
JSW 10.18 LGBB 8.51 LORG 4.46 TCIL 12.24
JNIL 5.72 LPSL 18.28 MMGL 7.78 VSCL 18.90
JCL 5.69 LMEL 4.19 MANGLMC 4.78 HEGL 14.07 L
MUSL 19.74 LSIL 13.73 HSIL 3.73
Sources: Prowess Database (CMIE Ltd). *Values is reported for RTN=/=>TQ for showing bi-directional value.
CONCLUSION
The present study examined the relationship between stock returns and trading volume in India using monthly data time series over a nine year period from January 2002 to December 2010 for three hundred forty seven Indian stocks by using descriptive statistics mean, median, maximum, minimum, standard deviation, skewness, kurtosis, jarque-bera, probability, sum, sum sq deviation, and granger causality tests. While the literature suggests the existence of significant interactions between the two variables and our result also shows that these to variable are inter-related in case of India.The result of present study shows that the two variables are co-integrated and dependent in most cases and movement in one variable affect the each other. There for investor can use information obtained from one variable (stock return) to predict the behaviour of other variable (trading volume).The analysis using granger causality method showed that there was co-integration between the two variables thus indicating that there seems to be long run relationship between the two variables. The granger causality test showed evidence of causality between the two. Such findings are in contrast with flow and stock oriented models of stocks exchanges determination which postulate a relationship between stock return and trading volume.Moreover the investors can utilise this relationship between stock return and trading volume to predict the behaviour of these variables. Theoretically, if there is any linkage between both of the variables then the crises can be averted either by managing trading volume or adopting indigenous policies to stable to stock market.
As we can see in the high degree positive relationship between two variables trading
volume and stock return during the period of from 2002 to 2010 in the Indian stock market. Therefore, there is a need to under take an in-depth research to understand the relationship for making return in stock markets.
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APPENDIX
TABLE: LIST OF CONSTITUENTS OF BSE
This table provides the list of constituents of 347 stocks of BSC, a value-weighted stock of Bombay Stock Exchange. Data period is Jan. 2002 to Dec. 2010.
Company Name Company Name Company Name
Ashok Leyland Ltd Aplab Ltd. Jhagadia Copper Ltd.
Tinplate Co. Of India Atlas Cycles (Haryana) Ltd. Ltd. Jindal Saw Ltd.
Tube Investments Of Eicher Motors Ltd. India Ltd. Jindal Steel & Power Ltd.
Hero Honda Motors Ltd. Usha Martin Ltd. Jyoti Structures Ltd.
Uttam Galva Steels Kalpataru Power Hindustan Motors Ltd. Ltd. Transmission Ltd.
V B C Ferro Alloys Kinetic Engineering Ltd. Ltd. Kalyani Steels Ltd.
Kirloskar Ferrous Inds. Kinetic Motor Co. Ltd. Welspun Corp Ltd. Ltd.
Western India Shipyard L G Balakrishnan& Bros. L M L Ltd. Ltd. Ltd.
Zenith Birla (India) Lakshmi Precision Screws Maharashtra Scooters Ltd. Ltd. Ltd.
Cochin Minerals & Mahindra & Mahindra Ltd. Rutile Ltd Lanco Industries Ltd.
Gujarat Mineral Devp. Lloyds Metals & Majestic Auto Ltd. Corpn. Ltd. Engineers Ltd.
Gujarat N R E Coke S M L Isuzu Ltd. Ltd. Lloyds Steel Inds. Ltd. www.zenithresearch.org.in
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Himadri Chemicals Scooters India Ltd. &Inds. Ltd. M M Forgings Ltd.
Hindustan Oil Magna Electro Castings Stone India Ltd. Exploration Co. Ltd. Ltd.
Maharashtra Seamless T V S Motor Co. Ltd. Insilco Ltd. Ltd.
Oil & Natural Gas Mahindra Ugine Steel Co. Tata Motors Ltd. Corpn. Ltd. Ltd.
Oriental Carbon & Man Industries (India) Ajanta Pharma Ltd Chemicals Ltd. Ltd.
Selan Exploration Andhra Petrochemicals Ltd. Technology Ltd. Metalman Industries Ltd.
Monnet Ispat& Energy Apollo Tyres Ltd. Sesa Goa Ltd. Ltd.
Arvind Remedies Ltd. A C C Ltd Mukand Ltd.
National Aluminium Co. Asian Paints Ltd. Ambuja Cements Ltd. Ltd.
National Steel & Agro Avon Organics Ltd. Andhra Cements Ltd. Inds. Ltd.
B A S F India Ltd. Aro Granite Inds. Ltd. Nile Ltd.
Bharat Petroleum Corpn. Ltd. Asahi India Glass Ltd. Oil Country Tubular Ltd.
Cadila Healthcare Ltd. Asian Star Co. Ltd. Oricon Enterprises Ltd.
Orissa Sponge Iron & Castrol India Ltd. Bell Ceramics Ltd. Steel Ltd.
Ceat Ltd. Birla Corporation Ltd. P G Foils Ltd.
Chambal Fertilisers & Chemicals Borosil Glass Works Ltd. Ltd. P S L Ltd.
Carborundum Chennai Petroleum Corpn. Ltd. Universal Ltd. Pennar Industries Ltd.
Century Textiles Cipla Ltd. &Inds. Ltd. Rajratan Global Wire Ltd.
Clariant Chemicals (India) Ltd. Rapicut Carbides Ltd. www.zenithresearch.org.in Classic Diamonds
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(India) Ltd.
Colgate-Palmolive (India) Ltd. Deccan Cements Ltd. Rathi Steel & Power Ltd.
Ratnamani Metals & D C W Ltd. Everest Industries Ltd. Tubes Ltd.
Goldiam International Ruchi Strips & Alloys Dabur India Ltd. Ltd. Ltd.
Dai-Ichi Karkaria Ltd. Graphite India Ltd. SathavahanaIspat Ltd.
Deepak Fertilisers& Petrochemicals Corpn. Ltd. Grindwell Norton Ltd. Shah Alloys Ltd.
Gujarat Sidhee Cement Dr. Reddy'S Laboratories Ltd. Ltd. Shetron Ltd.
E P I C Enzymes, Pharmaceuticals Shivalik Bimetal Controls &Indl. Chemicals Ltd. H E G Ltd. Ltd.
Essar Oil Ltd. H S I L Ltd. Siddhartha Tubes Ltd.
Heidelberg Cement Steel Authority Of India Excel Industries Ltd. India Ltd. Ltd.
I F G L Refractories G H C L Ltd. Ltd. Sterling Tools Ltd.
Sunflag Iron & Steel Co. Garware Polyester Ltd. India Cements Ltd. Ltd.
Glaxosmithkline Pharmaceuticals Indian Hume Pipe Co. Ltd. Ltd. Tata Metaliks Ltd.
J K Lakshmi Cement Godrej Consumer Products Ltd. Ltd. Tata Sponge Iron Ltd.
Gufic Biosciences Ltd. Kajaria Ceramics Ltd. Tata Steel Ltd.
Gujarat Narmada Valley Fertilizers Kakatiya Cement Sugar Co. Ltd. &Inds. Ltd. Tayo Rolls Ltd.
Gujarat State Fertilizers & S P E L Semiconductor Chemicals Ltd. La Opala R G Ltd. Ltd.
Madhav Marbles & Hindustan Organic Chemicals Ltd. Granites Ltd. Samtel Color Ltd.
Hindustan Petroleum Corpn. Ltd. Madras Cements Ltd. Sharp India Ltd. www.zenithresearch.org.in
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Hindustan Unilever Ltd. Mangalam Cement Ltd. Shyam Telecom Ltd.
Murudeshwar Ceramics Siemens Healthcare Ipca Laboratories Ltd. Ltd. Diagnostics Ltd.
J B Chemicals & Pharmaceuticals Smartlink Network Ltd. N C L Industries Ltd. Systems Ltd.
Switching Technologies Jain Irrigation Systems Ltd. O C L India Ltd. Gunther Ltd.
Kopran Ltd. Orient Abrasives Ltd. V X L Instruments Ltd.
Orient Ceramics &Inds. Valiant Communications Lupin Ltd. Ltd. Ltd.
Mangalore Chemicals & Fertilizers Orient Paper &Inds. Ltd. Ltd. Videocon Industries Ltd.
Mangalore Refinery & Petrochemicals Ltd. Parekh Platinum Ltd. Vintron Informatics Ltd.
Max India Ltd. Prism Cement Ltd. X O Infotech Ltd.
Morepen Laboratories Ltd. Rain Commodities Ltd. Zenith Computers Ltd.
Nagarjuna Fertilizers & Chemicals Zicom Electronic Security Ltd. Rajesh Exports Ltd. Systems Ltd.
Nocil Ltd. Ramco Industries Ltd. B F Utilities Ltd
Novartis India Ltd. Regency Ceramics Ltd. C E S C Ltd.
S B & T International Gujarat Industries Power Piramal Healthcare Ltd. Ltd. Co. Ltd.
Nava Bharat Ventures Ranbaxy Laboratories Ltd. Sagar Cements Ltd. Ltd.
Rashtriya Chemicals & Fertilizers Saint-Gobain Sekurit Neyveli Lignite Corpn. Ltd. India Ltd. Ltd.
Reliance Infrastructure Reliance Industries Ltd. Shree Cement Ltd. Ltd.
Shree Digvijay Cement Shree Rama Multi-Tech Ltd. Co. Ltd. Tata Power Co. Ltd.
Sterling Biotech Ltd. Shrenuj& Co. Ltd. Ahmednagar Forgings Ltd www.zenithresearch.org.in
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Sun Pharmaceutical Inds. Ltd. Shyam Star Gems Ltd. AliconCastalloy Ltd.
Bellary Steels & Alloys TamilnaduPetroproducts Ltd. Silver Smith India Ltd. Ltd.
Tata Chemicals Ltd. Somany Ceramics Ltd. Bhushan Steel Ltd.
Su-Raj Diamonds & Uflex Ltd. Jewellery Ltd. Bhuwalka Steel Inds. Ltd.
Suashish Diamonds Vidhi Dyestuffs Manufacturing Ltd. Ltd. Binani Industries Ltd.
Sunraj Diamond Coventry Coil-O-Matic Wockhardt Ltd. Exports Ltd. (Haryana) Ltd.
Aarvee Denims & Exports Ltd Titan Industries Ltd. De Nora India Ltd.
Alok Industries Ltd. Triveni Glass Ltd. Electrosteel Castings Ltd.
Amarjothi Spinning Mills Ltd. Vaibhav Gems Ltd. Ensa Steel Inds. Ltd.
Amit Spinning Inds. Ltd. Vesuvius India Ltd. Expo Gas Containers Ltd.
Arvind Ltd. Visaka Industries Ltd. Ferro Alloys Corpn. Ltd.
Gandhi Special Tubes Ashima Ltd. AnsalBuildwell Ltd Ltd.
Ansal Housing & Bhilwara Spinners Ltd. Construction Ltd. Gillette India Ltd.
Ansal Properties & Gontermann-Peipers D C M Ltd. Infrastructure Ltd. (India) Ltd.
Arihant Foundations & Hind AluminiumInds. EskayK'N'It (India) Ltd. Housing Ltd. Ltd.
D S Kulkarni
Eurotex Industries & Exports Ltd. Developers Ltd. Hindalco Industries Ltd.
H B Estate Developers G T N Industries Ltd. Ltd. Hindustan Tin Works Ltd.
Lok Housing & Gangotri Textiles Ltd. Constructions Ltd. Hindustan Zinc Ltd.
Ginni Filaments Ltd. Peninsula Land Ltd. India Foils Ltd. [Merged]
H P Cotton Textile Mills Ltd. www.zenithresearch.org.in Radhe Developers Investment & Precision
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(India) Ltd. Castings Ltd.
Hindoostan Spinning &Wvg. Mills Rajeswari Ltd. Infrastructure Ltd. Ispat Industries Ltd.
Indo Count Inds. Ltd. Unitech Ltd. J S W Steel Ltd.
K G Denim Ltd. A B C India Ltd Jai Corp Ltd.
Balurghat Technologies Lakshmi Mills Co. Ltd. Ltd. JayaswalNecoInds. Ltd.
Loyal Textile Mills Ltd. Blue Dart Express Ltd. I T I Ltd.
Chowgule Steamships Malwa Cotton Spg. Mills Ltd. Ltd. J C T Electronics Ltd.
Container Corpn. Of Lakshmi Electrical Maral Overseas Ltd. India Ltd. Control Systems Ltd.
Dredging Corpn. Of Linaks Microelectronics Minaxi Textiles Ltd. India Ltd. Ltd.
Essar Shipping Ports & Nagreeka Exports Ltd. Logistics Ltd. Mirc Electronics Ltd.
Garware Offshore Nahar Spinning Mills Ltd. Services Ltd. Moser Baer India Ltd.
Patspin India Ltd. Gati Ltd. Mro-Tek Ltd.
Prime Urban Development India Great Eastern Shipping Ltd. Co. Ltd. Nelco Ltd.
R S W M Ltd. Jagson Airlines Ltd. Opto Circuits (India) Ltd.
S Kumars Nationwide Ltd. Mercator Lines Ltd. P C S Technology Ltd.
Patel Integrated Punjab Communications
Sambandam Spinning Mills Ltd. Logistics Ltd. Ltd.
Shree Rajasthan Syntex Ltd. S K S Logistics Ltd. Rama Vision Ltd.
Shipping Corpn. Of Siyaram Silk Mills Ltd. India Ltd. Computer Point Ltd.
Shreyas Shipping & Soma Textiles &Inds. Ltd. Logistics Ltd. Cosmo Ferrites Ltd.
Spentex Industries Ltd. Sical Logistics Ltd. www.zenithresearch.org.in Dynacons Systems &
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Solutions Ltd.
Sri Lakshmi Saraswathi Textiles Gamma Infoway Exalt (Arni) Ltd. Spicejet Ltd. Ltd.
Transport Corporation Gemini Communication Super Sales India Ltd. Of India Ltd. Ltd.
Varun Shipping Co. Himachal Futuristic Super Spinning Mills Ltd. Ltd. Communications Ltd.
Suryajyoti Spinning Mills Ltd. Asian Electronics Ltd. Hind Rectifiers Ltd.
Honeywell Automation Suryavanshi Spinning Mills Ltd. Avantel Ltd. India Ltd.
A D C India T T Ltd. B P L Ltd. Communications Ltd
VardhmanPolytex Ltd. Bharat Electronics Ltd. A G C Networks Ltd.
Centum Electronics Vardhman Textiles Ltd. Ltd. AccelTransmatic Ltd.
Cerebra Integrated Winsome Yarns Ltd. Technologies Ltd.
www.zenithresearch.org.in
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