The Empirical Investigation of Causal Relationship between Intraday Return and Volume in Indian Stock Market

Abstract

This article examines the causal relationship between intraday return and volume by using 1-minute intraday data of 35 stocks of S&P CNX Nifty index during the period from April 2007 to March 2011. The empirical analysis provides evidence to the mixture of distribution hypothesis (MDH), as a majority of stocks of S&P CNX Nifty index show no causal relationship between the intraday return–volume relationships. However, this study finds evidence of significant causal and lead–lag relations between the intraday return–volume associations for some stocks. These findings reveal strong indication of uni- and bi-directional causality, thus supporting the sequential information arrival hypothesis (SIAH) which suggests that lagged values of volume provide the predictability component of current return (vice versa).

Keywords

High-frequency Data Granger Causality and Vector Autoregression Test Mixture of Distribution Hypothesis Sequential Information Arrival Hypothesis

Introduction

The proficiency of equity market is a principal prerequisite for the development of any economy. Understanding the efficiency has been on the target of investors and speculators for a long time. It has also been accredited time and again that return and volume are two major pillars around which the stock market revolves. While returns are interpreted as the evaluation of the new information, volume is an indicator to which the investors disagree about this information (Mahajan & Singh, 2009). Reviewing the combined dynamics of stock prices and trading volume is essential to improve the understanding of the microstructure of stock markets (Mestel et al., 2003). Return–volume associations are of mutual interest as they may excavate reliance that can form the basis of lucrative trading strategies, and this has implications for market efficiency (Chen, Firth & Yu, 2004).

Karpoff (1987) cited four reasons for focusing on price–volume relation. First, it provides insight into the structure of financial markets, such as the rate of information flow to the market, how the information is disseminated, the extent to which market prices convey the information and the existence of short sales constraints. Second, the relationship between price and volume can be used to examine the usefulness of technical analysis. Third, the price–volume relation is critical to the debate over the empirical distribution of speculative prices. And fourth, the unpredictable price–volume relationship may have important implications for moulding new agreements. In addition, the return–volume relationship sheds light on the efficiency of stock markets.

By examining these facets of the stock market, it can be understood that the inter-temporal causality relationship between return, volume and volatility sheds light on the informational proficiency of the market (Kocagil & Shachmurove, 1998). Movements in stock prices (commonly known as returns) and trading volume are influenced by the flow of new information into the market. A proposition of causality from volume to returns (vice versa) disrupts norms of the weak-form efficiency hypothesis.

Uni-directional causal relationship advocates that information transmits from volume to return/volatility, return to volume/volatility and from volatility to volume/return, and thus suggests that the lagged values of a measure provide the predictability component of the other measure. Thus, the feedback relationship between return, volume and volatility suggests that both variables encompass imperative material, which helps traders to price the value content of new information. A positive causal relation from returns to volume is consistent with the positive response trading strategies of noise traders, for whom the trading decision is based on past stock price movements (see Long, 1990; Lokman & Abdulnasser, 2005). On the other hand, bi-directional causality implies response relationship but it does not disprove the likelihoods of witnessing lead–lag relationship between volume and return.

Prior literature emphasizes on two theoretical explanations for the observed causal return–volume–volatility relations of stocks, namely, the mixture of distribution hypothesis (MDH) and the sequential information arrival hypothesis (SIAH). The SIAH of Copeland (1976), Jennings et al. (1981) and Smirlock and Starks (1985) assumes that traders receive new information in a random, unsystematic fashion. This class of models assume that information is observed by each trader sequentially and arbitrarily. This leads to the sequential reaction to information, accordingly suggesting that lagged values of volatility may have the ability to predict current trading volume/return, and vice versa. Conversely, the MDH advanced by Clark (1973), Harris (1987) and Anderson (1996) implies an alternative return–volatility–volume nexus. The model assumes all traders simultaneously receive the new price signals. As such, there should be no information content in past volatility data that can be used to forecast volume/return (vice versa), since these variables contemporaneously react to new information (Darrat, Rahman & Zhong, 2003).

These theories of SIAH and MDH provide a detailed understanding of the relationship between intraday return, volume and volatility and may help investors to recognize potential patterns of the stock market which can be used in their speculation choices. Second, the intraday price–volume relation can also be used as a basis of trading strategy for checking the efficiency of stock markets. Third, the relationship between intraday return and volume can be used to scrutinize the utility of technical analysis. Evidence states there is a dearth of studies which have examined the causal relationships on an intraday basis (see Table 1). This article is thus, to the best of our knowledge, one of the first efforts to examine the causal relation between intraday return and volume by using 1-minute intraday data of 35 stocks of S&P CNX Nifty index. This intraday analysis will shed light on the efficiency of the Indian stock market.

Review of Literature

An examination of potential causality from past values of volume to present returns as well as from past returns to present volume is concerned with issues relating to informational efficiency of the market (Mcmillan & Speight, 2002). An indication of causality from past values of volume to returns violates assumptions of the weak-form efficiency hypothesis, since it carries the implication that an investor is able to make systematic profits.

Voluminous literature is available (see Table 2) with respect to studies which have reported the evidence (and/or in evidence) of causality between return, volume and volatility with low-frequency data (daily, weekly and monthly). The intraday-based analysis (minute, hourly) is a relatively new phenomenon in the stock market. There is a dearth of literature (see Table 1) with respect to high-frequency-based studies, particularly in the Indian context. This study thus aims to plug this literature gap by empirically examining the intraday return and volume measures of the Indian equity market.

The studies conducted at low-frequency time interval which have reported no causal relationship are those by Granger et al. (1964) who tested the relationship between the price and volume of individual stocks and found no relationship between the two. Chen et al. (2004) reported evidence of no causal relation between return and volume series of China’s stock market. Otavio (2006) further supported the MDH by observing no causality relationship between return and volume for the Bovespa index of Brazil. This finding was further supported by another study at the Brazilian stock market by Medeiros and Doornik (2008) who reported no causality evidence as well. These studies at daily frequencies are in conformance to the MDH, which suggests that the information dissemination is contemporaneous. In other words, futures prices (and volume) only change when information arrives, and they evolve at a constant speed in event time (Sutcliffe, 1993). The MDH implies only a contemporaneous relationship between volume and (absolute) returns, where the shift to a new equilibrium is immediate. It is associated with research efforts of Clark (1973), Epps and Epps (1976), Tauchen and Pitts (1983) and Harris and Gurel (1986).

Empirical studies which report a uni-directional causal relationship from return to volume data sampled at daily frequencies are those of Tauchen and Pitts (1983), Karpoff (1987), Gallant et al. (1992), Lamoureux and Lastrapes (1994) and Jones et al. (1994). These studies support the notion of Copeland (1976) who introduced the SIAH with asymmetrically distributed information in which information flows sequentially from one trader to another. Other eminent empirical studies, which reported similar results while examining the price–volume relationship are those by Lee and Rui (2002), Ciner (2003), Mishra (2004), Ramaprasad and Shigeyuki (2004), Gurgul et al. (2005), Tambi (2005), Nguyen and Diagler (2006), Anirut and Abeyratna (2006) and Mahajan and Singh (2008).

Table 1.

Empirical Evidence on Causal Relationship between Intraday Return, Volume and Volatility

Authors	Year	Country	Sample Data/Database	Sample Period	Differencing Interval	Causal Relationship	Causal Relationship	Causal Relationship
Authors	Year	Country	Sample Data/Database	Sample Period	Differencing Interval	Intraday Return and Volume	Intraday Volume and Volatility	Intraday Volatility and Return
Mcmillan and Speight	2002	UK	Short sterling contracts and FTSE-100	1992–1995	1-minute interval	UDC from volume to return	BDC	NE
Spierdijk	2002	USA	NYSE Trade and Quote (TAQ) database	August, September and October, 1999	5-second interval	BDC	NE	NE
Darrat et al.	2003	USA	30 DJIA stocks	1998	1-minute interval	NE	UDC from volume to volatility	NE
Chae and Joo	2003	USA	KRW/USD	2001–2002	2-minute interval	NE	BDC	NE
Chiang et al.	2006	USA	NASDAQ (Trade and Quotation [TAQ] database)	2001–2004	5-minute interval	NE	BDC	NE
Dufour et al.	2006	Spain	S&P 500 Index futures traded on CSE	1988–2005	5-minute interval	NE	BDC	NE
Lu et al.	2010	Taiwan	TWSE	2005–2007	1-minute interval	NE	BDC	NE
Lu and Lin	2010	Australia	TWSE database	2005–2007	1-minute interval	NE	BDC	NE
Hatrick	2010	Hong Kong	HSBC	2009–2009	10-, 20- and 30-minute intervals	NE	UDC from volume to volatility	NE
Dufour, Garcia and Taamouti	2011	USA	S&P 500 Index futures contracts	1988–2005	5-minute interval	NE	NE	BDC.
Celik	2013	Istanbul	ISE	2005–2010	5-minute interval	NE	NC = pre-crisis period, BDC = crisis period	NE

Source: Compiled from various studies.

Note: LIFFE—London International Financial Futures and Options Exchange, FTSE—share index of the 100 companies listed on the London Stock Exchange with the highest market capitalization, DJIA—Dow Jones Industrial Average, KRW/USD—Spot Korean won against US Dollar Exchange Rate, ISE—Istanbul Stock Exchange, TWSE—Taiwan Stock Exchange, NASDAQ—National Association of Securities Dealers Automated Quotations, HSBC—Hong Kong Stock Exchange, NYSE—New York Stock Exchange, DAX—Deutscher Aktien Index, CAC—Continuous Assisted Quotation (French Stock Index), UKX—United Kingdom Index, BUX—Budapest Stock Exchange, PX-50—Prague Stock Exchange Index, WIG-20—Warsaw Stock Exchange (top traded 20 Polish stocks), CSE—Chicago Mercantile Exchange, NE—not examined, NC—no causality, UDC—uni-directional causality and BDC—bi-directional causality.

Table 2.

Empirical Evidence on Causal Relationship between Return, Volume and Volatility

Authors	Year	Country	Sample Data/Database	Sample Period	Differencing Interval	Causal Relationship	Causal Relationship	Causal Relationship
Authors	Year	Country	Sample Data/Database	Sample Period	Differencing Interval	Return and Volume	Volume and Volatility	Volatility and Return
Malliaris and Urrutia	1992	USA	S&P 500, Nikkei, FT-30, Hang Seng, The Straits Times and All Ordinaries Indices	1987	Daily	NC = pre-crash and post-crash periods. UDC = October 1987	NE	NE
Hiemstra and Jones	1994	USA	Dow Jones Stock Price Index	1915–1946 1947–1990	Daily	BDC	NE	NE
Bhanupant	2001	India	S&P CNX Nifty index	1996–2002	Daily	BDC	NE	NE
Ciner	2002	Canada	TSE	1990–2002	Daily	NC	UDC from volume to volatility	NE
Lee and Rui	2002	USA	S&P 500, TOPIX and FTSE 100 indices	1973–1999 1974–1999 1986–1999	Daily	UDC from return to volume for US and Japanese markets only	BDC	NE
Mestel et al.	2003	Austria	31 common stocks in ASE	2000–2003	Daily	BDC	UDC from volatility to volume	NE
Ciner	2003	USA	KLSE TSE*	1992–2001 1993–2002	Daily	UDC from volume to return	UDC from volume to volatility	NE
Mishra	2004	India	Seven companies at CNXIT of NSE	2000–2003	Daily	UDC from return causes volume for four stocks and UDC from volume causes for two stocks	UDC from volume to volatility	NE
Ramaprasad and Shigeyuki	2004	Netherland	Gold futures contracts	1990–2000	Daily	UDC from return to volume	NE	NE
Gurgul et al.	2005	Poland	WIG 20	1995–2005	Daily	UDC from return to volume	UDC from volatility to volume	NE
Chen et al.	2005	China	China’s commodity futures. Copper, soybean, aluminium and wheat	1996–2002	Daily	NC	UDC from volatility to volume	NE
Tambi	2005	India	Nifty index	2000–2005	Daily	UDC form volume to return	NE	NE
Gunduz and Hatemi	2005	USA	Stock markets in the Czech Republic, Hungary, Poland, Russia and Turkey	1988–2002	Daily	NC = Czech Republic BDC = Hungary and Poland UDC = Russia and Turkey	NE	NE
Nguyen and Diagler	2006	USA	S&P 500 and NASDAQ	1998–2002	Daily	UDC from return to volume	BDC	NE
Otavio and Bernardus	2006	Brazil	Bovespa index	2000–2005	Daily	NC	BDC	NE
Olan and Michael	2006	China	21 stocks of Hang Seng index	1994–2001	Daily	NE	BDC	NE
Anirut and Abeyratna	2006	Australia	JSECI, KLSECI, MSECI, DMI and BSETI	1991–2004 1990–2004	Daily	UDC from return to volume	NE	NE
Mahajan and Singh	2007	India	Nifty index	2001–2006	Daily	UDC from return to volume	NE	NE
Konan	2007	West Africa	BRVM	2002–2005	Daily	NE	UDC from volume to volatility	NE
Floros	2008	Greece	ADEX	1997–2001	Daily	BDC	NE	NE
Mahajan and Singh	2008	India	Sensex	1996–2007	Daily	UDC from volume to return	UDC from volatility to volume	NE
Mahajan and Singh	2008	India	Nifty index	2001–2006	Daily	NE	UDC from volume to volatility	NE
Mahajan and Singh	2008	India	Nifty index	2000–2006	Daily	BDC	UDC from volatility to volume	NE
Medeiros and Doornik	2008	Brazil	Bovespa index	2000–2005	Daily	NC	BDC	Ne
Kumar et al.	2009	India	S&P CNX Nifty index	2000–2008	Daily	BDC	NE	NE
Dey and Wang	2010	USA	NYSE	2010	Daily	NE	UDC from volume to volatility	NE
Habib	2011	Egypt	ESE	1998–2005	Daily	NE	BDC	NE
Rajput et al.	2012	India	S&P CNX Nifty index	2003–2012	Daily spot and futures prices	UDC from return to volume	NE	NE
Hatekar et al.	2013	India	S&P BSE SENSEX	2011–2013	Daily	UDC from volume to returns	NE	NE
Gurgul and Syrek	2013	Poland	WSE	2008–2011	Daily	BDC	BDC	UDC from volatility to return
Ananzeh et al.	2013	Jordan	ASE	2002–2012	Daily	NE	BDC	NE
Bruggemann et al.	2013	Switzerland	European countries	1990–2012	Daily	BDC	NE	NE

Source: Compiled from various studies.

Note: BSETI—Bangkok SET Index for Thailand, BRVM—Bourse Regionale des Valeurs Mobilieres, DJIA—Dow Jones Industrial Average, DMI—Data Stream Market Index for Singapore, JSECI—Jakarta Stock Exchange Composite Index for Indonesia, KRW/USD—Spot Korean won against US Dollar Exchange Rate, KLSE—Kuala Lumpur Stock Exchange, KLSECI—Kuala Lumpur Stock Exchange Composite Index for Malaysia, LDB—Liquidity Data Bank, MSECI—Manila Stock Exchange Composite Index for the Philippines, NYSE—New York Stock Exchange, NSE—National Stock Exchange, TOPIX—Tokyo Stock Exchange Price Index, TSE—Toronto Stock Exchange, TSE*—Tokyo Stock Exchange, WIG 20—the 20 most liquid companies quoted on the primary market of the Warsaw Stock Exchange, NA—not available, NE—not examined, ISE—Istanbul Stock Exchange, ESE—Egyptian Securities Exchange, WSE—Warsaw Stock Exchange, BSI—BOVESPA (Brazilian Stock Index), ADEX—the Athens Derivatives Exchange, TWSE—Taiwan Stock Exchange, ASE—Amman Stock Exchange, GIRFs—generalized impulse response functions, ASE—Austrian stock market, NC—no causality, UDC—uni-directional causality and BDC—bi-directional causality.

The empirical works which observed bi-directional causality between measures of return, volume and volatility are those of Malliaris and Urrutia (1992), Bhanupant (2001), Chen et al. (2001), Mestel et al. (2003), Mahajan and Singh (2008b), Kumar et al. (2009), Gurgul and Syrek (2013), Floros (2008) and Bruggermann et al. (2013). These empirical evidences support the SIAH and reject the MDH of Clark (1973). These studies imply that news is revealed to investors sequentially, not simultaneously, which in turn initiates a sequence of transitional price equilibrium compounded by the persistence of high trading volumes. Due to the sequential nature of the process, lagged trading volume may help to predict current stock returns, as much as lagged returns can predict current volume (see Copeland, 1976; Hatekar, Gupta, Suryavanshi & Tiwari, 2013; Mahajan & Singh, 2008c; Rajput, Kakkar, Batra & Gupta, 2012).

The causal relationship between these measures has been examined on an intraday basis by Darrat et al. (2003) who studied 30 Dow Jones Industrial Average (DJIA) stocks at 1-minute interval and found uni-directional causality from volume to volatility. Giot (2005) fitted generalized autoregressive conditional heteroskedasticity (GARCH) models to de-seasonalized 15- and 30-minute returns. Extra complication when dealing with intraday volume and volatility lies in the handling of the intraday seasonality and market microstructure noise. Darrat et al. (2007) examined the Granger causality between volume and volatility using 1-month New York Stock Exchange (NYSE) tick data.

Further, Hatrick (2010) studied the stocks listed on the Hong Kong stock exchange at intraday time intervals of 10, 20 and 30 minutes and found evidence of uni-directional causality from volume to volatility, thus conforming to the notion that lagged values of volume (volatility) have a forecasting power for the volatility (volume) measure. Further Celik (2013) found evidence of bi-directional causality at 5-minute interval analysis. These studies conformed to the sequential information arrival pattern for these markets. In addition to these empirical investigations, outstanding inspections on the use of high-frequency financial data sets in financial econometrics are provided by Mulherin and Gerety (1988), Jones et al. (1994), Campbell et al. (1997), Engle and Russell (1998), Darolles et al. (2006), Anderson (2000), Wood (2000), Ghysels (2000), Dacarogna et al. (2001), Gouri´eroux and Jasiak (2001), Lyons (2001), Tsay (2001) and Ghysels et al. (2014). As can be seen from Table 1, there is a literature gap with respect to intraday-based studies in the context of the Indian equity market. This article aims to plug in the same.

Data and Methodology

This study is based on 1-minute interval data of 35 stocks listed on S&P CNX Nifty index, during the period from 1 April 2010 to 31 March 2011. The S&P CNX Nifty index is a well-differentiated 50-stock index precisely imitating general market environments, characterized by very dynamically dealt stocks. This study applies the filters of bonus issue and stock split announcement and derives a concise and well-balanced sample of 35 stocks. The intraday stock return parameter comprises continuous rates of return, computed as log of ratio of present minute’s price to previous minute’s price (i.e., Rt = ln (Pt/Pt – 1)). Volume is taken as per the trading volume data.

Empirical Findings and Analysis

The examination of the relationship between return and volume provides significant information regarding the price discovery efficiency of an asset. The summary statistics of the sample stocks suggests that dispersion of mean returns represents trivial values, thereby indicating a steady index. This authorizes the belief that the index under examination is a very safe index. Substantial Jarque–Bera statistics undoubtedly discards the hypothesis, which suggests that all variables do not conform to normal distribution, which is the precondition for any market to be efficient in the weak form (Fama, 1965; Kamath et al. 1998; Reddy, 1997; Stevenson & Bear, 1970).

Further, the statistics of skewness and kurtosis preserve the substantiation of withdrawal from normality hypothesis. The realistic distribution of the return and volume series in this article is positively skewed, indicating a right tail of distribution. These positively skewed series accentuate the asymmetric nature of the series. Additionally, the excess kurtosis projected for trading volume series is a strong indication of leptokurtic peaked series. The extreme kurtosis values of Indian markets demonstrate extraordinary erraticism in the returns measure. The results of this article thus suggest the high probability of extreme values (profit/loss) occurring. These results further stress that only the chief and dominant players attained the core of trading activity, while the small-time traders could not participate actively in the trading activity.

For the stocks of S&P CNX Nifty index, this article verifies the existence (inexistence) of inter-temporal relationship between intraday return and volume series. This study applies the Granger causality test for determining whether one intraday measure can be used to forecast the other measure. For this, the precondition is that the series under study should be stationary in nature. Therefore, the augmented Dickey–Fuller (ADF) unit root test results are estimated on the basis of the technique. The unit root test results shows that intraday return and volume are stationary at 1 and 5 per cent levels of significance, respectively, for all the 35 stocks comprising the sample.

For the S&P CNX Nifty index, this article verifies the existence of relationship and direction of information flow between intraday return and volume with the Granger causality test, which determines whether one intraday series is useful in forecasting another. The lags for the Granger causality test are estimated according to the Schwarz information criterion (SIC). Further, the extent of lead–lag relationships is estimated with the vector autoregression (VAR) test.

An analysis of Table 3 reveals that no causality was observed for 22 stocks amounting for 62.85 per cent of the sample (namely, ACC, AXISBANK, BANKBARODA, BHEL, BPCL, CAIRN, CIPLA, COALINDIA, HCLTECH, HDFCBANK, HEROMOTOCO, HINDUNILVR, ICICIBANK, IDFC, INDUSINDBK, INFOSYSTCH, LT, MARUTI, PNB, POWERGRID, SSLT and TATAPOWER). These results are further supported by the VAR results which confirm no causal relationship between intraday return and volume measures for these stocks. These stocks, which exhibit no causality, are in conformance to the mixture of distributions hypothesis, which suggests that the information dissemination in the Indian equity market is synchronous. In other words, futures returns (and volume) only change when information arrives, and they evolve at a constant speed in event time (Sutcliffe, 1993). The MDH implies only a coexistent relationship between volume and (absolute) returns. It is associated with Clark (1973), Epps and Epps (1976), Tauchen and Pitts (1983) and Harris (1986).

Table 3 depicts Granger causality test results for stocks depicting uni-directional causality between intraday return and volume. The results of this study show that 10 stocks comprising 28.57 per cent of the index constitute this sample. Out of these, three stocks exhibit a uni-directional causality from intraday return to volume (namely, ASIANPAINT, GRASIM and RANBAXY) which amount to 8.57 per cent of total sample size. The VAR results ascertain that for stock ASIANPAINT and RANBAXY, intraday return leads volume by 1 lag (1 minute). In case of GRASIM, intraday return leads volume at 2 lags (2 minutes). The findings of the present study of uni-directional causality from intraday return to volume are consistent with prior empirical findings of Lee and Swaminathan (2000), Lee and Rui (2002), Mcmillan and Speight (2002), Mestel et al. (2003) and Otavio et al. (2006).

Additionally, seven stocks show uni-directional Granger causality from intraday volume to return (see Table 3) amounting to 20 per cent of sample, namely, AMBUJACEM, DLF, DRREDDY, HINDALCO, NTPC, ULTRACEMCO and WIPRO. The VAR results identify the extent of lead–lag relation between these stocks. For stock AMBUJACEM, HINDALCO and ULTRACEMCO, intraday volume can predict return by 2 lags (2 minutes). For stocks DLF, DRREDDY and WIPRO, intraday volume leads return by 1 lag (1 minute). In the case of NTPC, the intraday volume series leads return by 3 lags (3 minutes).

Thereafter, this study identifies (see Table 3) that only three stocks constituting 8.57 per cent of total sample size observe bi-directional Granger causality between intraday stock return and volume (namely, SBIN, TATAMOTORS and TATASTEEL). The VAR test results depict that for SBIN, the intraday return and volume series exhibit response causality. Here, we note that intraday return can predict volume by 4 lags (4 minutes), whereas intraday volume can predict return by 10 lags (10 minutes). This indicates that intraday volume leads return by 6 lags (6 minutes). Furthermore, for TATAMOTORS, intraday return causes volume by 3 lags (3 minutes) and intraday volume causes return by 2 lags (2 minutes). Therefore, it can be concluded that intraday return leads volume by 1 lag (1 minute). In case of TATASTEEL, both intraday return and volume cause each other at 2 lags (2 minutes) showing that regardless of the existence of a two-way causality, there is no lead–lag relationship between the intraday variables of this stock.

Table 3.

Granger Causality Results for Intraday Return and Volume

Symbol	Null Hypothesis	No. of Lags	F-value	P-value
ACC	VOLUME does not Granger Cause RETURN	14	0.70344	0.7732
	RETURN does not Granger Cause VOLUME		0.53736	0.9127
AMBUJACEM	VOLUME does not Granger Cause RETURN	5	2.66745**	0.0204
	RETURN does not Granger Cause VOLUME		0.33991	0.8890
ASIANPAINT	VOLUME does not Granger Cause RETURN	1	0.02421	0.8764
	RETURN does not Granger Cause VOLUME		5.06791**	0.0244
AXISBANK	VOLUME does not Granger Cause RETURN	2	0.14019	0.8692
	RETURN does not Granger Cause VOLUME		0.02111	0.9791
BANKBARODA	VOLUME does not Granger Cause RETURN	13	0.15472	0.9998
	RETURN does not Granger Cause VOLUME		0.89590	0.5568
BHEL	VOLUME does not Granger Cause RETURN	3	0.52745	0.6634
	RETURN does not Granger Cause VOLUME		0.21523	0.8859
BPCL	VOLUME does not Granger Cause RETURN	2	0.02851	0.9719
	RETURN does not Granger Cause VOLUME		0.27569	0.7590
CAIRN	VOLUME does not Granger Cause RETURN	14	0.67543	0.8008
	RETURN does not Granger Cause VOLUME		0.57166	0.8892
CIPLA	VOLUME does not Granger Cause RETURN	3	0.03512	0.9912
	RETURN does not Granger Cause VOLUME		0.61261	0.6067
COALINDIA	VOLUME does not Granger Cause RETURN	2	2.61283	0.0700
	RETURN does not Granger Cause VOLUME		1.08297	0.3386
DLF	VOLUME does not Granger Cause RETURN	1	4.58479**	0.0323
	RETURN does not Granger Cause VOLUME		0.00563	0.9402
DRREDDY	VOLUME does not Granger Cause RETURN	1	5.71641**	0.0168
	RETURN does not Granger Cause VOLUME		0.00102	0.9745
GRASIM	VOLUME does not Granger Cause RETURN	3	0.60573	0.6112
	RETURN does not Granger Cause VOLUME		7.19841*	8.E-05
HCLTECH	VOLUME does not Granger Cause RETURN	3	1.22088	0.3003
	RETURN does not Granger Cause VOLUME		0.09813	0.9611
HDFCBANK	VOLUME does not Granger Cause RETURN	10	0.31036	0.9789
	RETURN does not Granger Cause VOLUME		0.14195	0.9992
HEROMOTOCO	VOLUME does not Granger Cause RETURN	6	1.93151	0.0718
	RETURN does not Granger Cause VOLUME		0.01048	1.0000
HINDALCO	VOLUME does not Granger Cause RETURN	20	2.31510*	0.0007
	RETURN does not Granger Cause VOLUME		1.38490	0.1168
HINDUNILVR	VOLUME does not Granger Cause RETURN	3	0.55632	0.6439
	RETURN does not Granger Cause VOLUME		1.83274	0.1387
ICICIBANK	VOLUME does not Granger Cause RETURN	12	0.45802	0.9393
	RETURN does not Granger Cause VOLUME		0.98813	0.4572
IDFC	VOLUME does not Granger Cause RETURN	12	0.59088	0.8516
	RETURN does not Granger Cause VOLUME		0.63584	0.8133
INDUSINDBK	VOLUME does not Granger Cause RETURN	1	0.25036	0.6168
	RETURN does not Granger Cause VOLUME		0.30369	0.5816
INFOSYSTCH	VOLUME does not Granger Cause RETURN	1	0.05846	0.8090
	RETURN does not Granger Cause VOLUME		1.9E-08	0.9999
LT	VOLUME does not Granger Cause RETURN	11	0.54023	0.8772
	RETURN does not Granger Cause VOLUME		0.12990	0.9997
MARUTI	VOLUME does not Granger Cause RETURN	13	0.88559	0.5679
	RETURN does not Granger Cause VOLUME		0.59366	0.8615
NTPC	VOLUME does not Granger Cause RETURN	31	3.70060*	2.E-11
	RETURN does not Granger Cause VOLUME		0.53195	0.9846
PNB	VOLUME does not Granger Cause RETURN	8	0.26287	0.9777
	RETURN does not Granger Cause VOLUME		0.42290	0.9081
POWERGRID	VOLUME does not Granger Cause RETURN	13	0.97933	0.4688
	RETURN does not Granger Cause VOLUME		0.56871	0.8808
RANBAXY	VOLUME does not Granger Cause RETURN	13	1.25704	0.2312
	RETURN does not Granger Cause VOLUME		1.69836**	0.0442
SBIN	VOLUME does not Granger Cause RETURN	49	64.1840*	00000
	RETURN does not Granger Cause VOLUME		88.1478*	00000
SSLT	VOLUME does not Granger Cause RETURN	46	0.20654	1.0000
	RETURN does not Granger Cause VOLUME		0.73961	0.9042
TATAMOTORS	VOLUME does not Granger Cause RETURN	3	5.82343*	0.0006
	RETURN does not Granger Cause VOLUME		8.07918*	2.E-05
TATAPOWER	VOLUME does not Granger Cause RETURN	17	0.30009	0.9975
	RETURN does not Granger Cause VOLUME		0.21736	0.9997
TATASTEEEL	VOLUME does not Granger Cause RETURN	2	2019.45*	00000
	RETURN does not Granger Cause VOLUME		62.5916*	4.E-28
ULTRACEMCO	VOLUME does not Granger Cause RETURN	4	2.39236**	0.0483
	RETURN does not Granger Cause VOLUME		0.97947	0.4172
WIPRO	VOLUME does not Granger Cause RETURN	14	4.59815*	2.E-08
	RETURN does not Granger Cause VOLUME		0.65607	0.8190

Source: Authors’ Calculations.

Note: *Significant at 1% level of significance, **significant at 5% level of significance and ***significant at 10% level of significance.

For these stocks exhibiting a bi-directional causality, news is revealed to investors sequentially, which originates a sequence of transitional price equilibrium is compounded by the persistence of high trading volumes. Due to the sequential nature of the process, lagged trading volume may help to predict current stock returns, as much as lagged returns can predict current volume (Copeland, 1976). The results of this study are consistent with empirical findings of Gallant et al. (1992), Wang (1994), Chordia and Swaminathan (2000), Chen, Firth and Rui (2001), Llorente et al. (2002), Gunduz and Hatemi (2005), Pisedtasalasai and Gunasekarage (2008) and Deo et al. (2008). Studies by Mulherin and Gerety (1988), Foster and Viswanathan (1993), Darolles et al. (2006) and Ghysels et al. (2014) have reported similar outcomes of bi-directional causality between intraday return and volume on an intraday basis.

Conclusion

It is well understood that the activity of the stock market cannot be decided only on the basis of prices. Stock prices deprived of their related volume measures deliver ambiguous information about market movement. Thus, this article examines the inter-temporal relationship between intraday return and volume of stock market by using 1-minute interval data for the S&P CNX Nifty index of the National Stock Exchange (NSE, India’s primary stock exchange).

Table 4 provides a clear pictorial depiction of the direction of flow of information between the measures of intraday return and volume. This study concludes that stocks which exhibit no causality between the measure of intraday return and volume are in conformance to the MDH, which suggests that the information dissemination in the Indian equity market is synchronous. In other words, futures returns (and volume) only change when information arrives, and they evolve at a constant speed in event time (Sutcliffe, 1993). Further, the stocks which depict unibidirectional causality conform to Copeland’s (1976) SIAH. This hypothesis caters to asymmetrically distributed information in which information flows sequentially from one trader to another. In other words, traders estimate the availability of private information using past periods’ trading volume and use this information to adjust their strategies. These stocks support the theory that the trading volume-based information is crucial for affecting the behaviour of the liquidity traders.

Table 4.

Direction of Flow of Information between Intraday Return and Volume

Bi-directional CausalityR → VV → R	Uni-directional CausalityR → V	Uni-directional CausalityV → R	No Causality
SBINTATAMOTORSTATASTEEL	ASIANPAINTGRASIMRANBAXY	AMBUJACEMDLFDRREDDYHINDALCONTPCULTRACEMCOWIPRO	ACCAXISBANKBANKBARODABHELBPCLCAIRNCIPLACOALINDIAHCLTECHHDFCBANKHEROMOTOCOHINDUNILVRICICIBANKIDFCINDUSINDBKINFOSYSTCHLTMARUTIPNBPOWERGRIDSSLTTATAPOWER

Source: Authors’ Calculations.

Note: R is return and V is volume.

With the exploration into these intraday causal relationships of S&P CNX Nifty index, market investors and regulators can obtain a healthier understanding of the risk evolution of their financial exposure in trading sessions within a day. This implies that based on the information of intraday return and volume, these market speculators are able to implement an improved risk management exercise. The results of this study are supportive of NSE’s transparent transactions combined with lower operational prices and efficiency, which have greatly increased the attractiveness of the Indian stock market to domestic and international investors.

References

Ananzeh

, Jdaitawi

, & Al-Jayousi

(2013). Relationship Between Market Volatility and Trading Volume: Evidence from Amman Stock Exchange. International Journal of Business and Social Science, 4(16), 188–198.

Anderson

T.G.

(1996). Return volatility and trading volume: An information flow interpretation of stochastic volatility. The Journal of Finance, 51(1), 169–204. Retrieved 12 July 2012, from http://onlinelibrary.wiley.com/doi/10.1111/j.1540-6261.1996.tb05206.x/abstract/

Anirut

, & Abeyratna

(2006). The causal and dynamic relationship between stock returns and trading volume: Evidence from emerging markets in South-East Asia (Working Paper). Presented at the 2006 AFAANZ Conference, Wellington, New Zealand, 2–4 July 2006. Retrieved 13 August 2013, from http://www.afis.canterbury.ac.nz/publications.pdf

Bhanupant. (2001). Testing dynamic relationship between returns and trading volume on the National Stock Exchange. Retrieved 21 August 2013, from www.utiicm.com/bhanupant.html

Bruggermann

, Glaser

, Schaarschmidt

& Stankiwwicz

(2013). Asymmetries in Stock Returns, Trading Volume Relation: A Generalized Impulse Response Approach. Retrieved 3 January 2014, from https://www.eurofidai.org/e/Schaarschmidt_2013.pdf

Campbell

J.Y.

, Lo

& MacKinlay

A.C.

(1997). The Econometrics of Financial Markets. Princeton, N.J: Princeton University Press.

Celik

(2013). New Evidence on the Relation between Trading Volume and Volatility. Journal of Business and Economic Research, 3(1), 176–186.

Chan

, & Fong

W.M.

(2000). Trade size, order imbalance, and the volatility-volume relation. Journal of Financial Economics, 57(2), 247–273.

Chen

Firth, G.M.

, & Rui

(2001). The Dynamic Relation between Stock Returns, Trading Volume and Volatility. The Financial Review, 36(3), 153–174.

10.

Chen

, Firth

, & Rui

(2001). The dynamic relation between stock returns, trading volume and volatility. The Financial Review, 36(3), 153–174.

11.

Chen

, Firth

, & Yu

(2004). The price–volume relationship in China’s commodity futures markets. The Chinese Economy, 37(3), 87–122.

12.

Chiang

T. C.

, Zhuo

Qiao

, & Wing-Keung

Wong

. (2006). New evidence on the relation between return volatility and trading volume. Journal of Forecasting, 29(5), 502–515.

13.

Chordia

, & Swaminathan

(2000). Trading volume and cross-autocorrelations in stock returns. Journal of Finance, 55(2), 913–935.

14.

Ciner

(2002a). The stock price-volume linkage on the Toronto stock exchange: Before and after automation. Review of Quantitative Finance and Accounting, 19(4), 335–349.

15.

Ciner

(2002b). Information content of volume: An investigation of Tokyo commodity futures markets. Pacific-Basin Finance Journal, 10(2), 201–215.

16.

Ciner

(2003). The Dynamic Relationship between Stock Prices and Trading Volume: An Examination of Call Markets. Retrieved 3 February 2013, from http://207.36.165.114/Denver/Papers/callmarkets.pdf

17.

Clark

P.K.

(1973). A subordinated stochastic process model with finite variance for speculative prices. Econometrica, 4(1), 135–155.

18.

Copeland

(1976). A model of asset trading under the assumption of sequential information arrival. Journal of Finance, 31(4), 1149–1168.

19.

Dacorogna

Michel M.

, . (2001). Extremal forex returns in extremely large data sets. Extremes 4(2) ,105–127.

20.

Darolles

, Gourieroux

, & Jasiak

(2006). Structural Laplace Transform and Compound Autoregressive Models. Journal of Time Series Analysis, 27(4), 477–503.

21.

Darrat

A.F.

, Rahman

, & Zhong

(2003). Intraday trading volume and return volatility of the DJIA stocks: A note. Journal of Banking and Finance, 27(10), 2035–2043.

22.

Deo

, Srinivasan

, & Devanadhen

(2008). The Empirical Relationship between Stock Returns, Trading Volume and Volatility: Evidence from Select Asia-Pacific Stock Market. European Journal of Economics, Finance and Administrative Sciences, 12(October), 23–41.

23.

Dey

M.K.

, & Wang

(2009). Volume Volatility in Dual Markets: Lessons from Chinese ADRs. Retrieved 12 January 2013, from http://ifrogs.org/PDF/EMF2010/DeyWang2010.pdf

24.

Dufour

, Garcia

, & Taamouti

(2011). Measuring high-frequency causality between returns, realized volatility and implied volatility. Retrieved 19 February 2013, from http://facultyresearch.edhec.com/mailing/drd/rg/Dufour_Garcia_Taamouti_2006_JFEtx_CausalityReturnsVolatility_2011_06_29.pdf

25.

Dufour

J.M.

, Pelletier

, & Renault

(2006). Short Run and Long Run Causality in Time Series: Inference. Journal of Econometrics, 132(2), 337–362.

26.

Engle

R.F.

, & Russell

J.R.

(1998). Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data. Econometrica, 66(5), 1127–1162.

27.

Epps

, & Epps

(1976). The stochastic dependence of security price changes and transaction volumes: Implications for the mixture-of-distributions hypothesis. Econometrica, 44(2), 305–321.

28.

Fama

(1965). The Behavior of Stock-Market Prices. The Journal of Business, 38(1), 34–105.

29.

Floros

(2008). Stock Returns and Inflation in Greece. Applied Econometrics and International Development, 4(2) 55–68.

30.

Foster

, & Viswanathan

(1993). Variations in Trading Volume, Return, Volatility and Trading Costs: Evidence on Recent Price Formation Models. Journal of Finance, 48(1), 187–211.

31.

Gallant

A.R.

, Rossi

P.E.

, & Tauchen

(1992). Stock Prices and Volume. Review of Financial Studies, 5(2), 199–242.

32.

Gunduz

, & Hatemi-J

(2005). Stock Price and Volume Relation in Emerging Markets. Emerging Markets Finance and Trade, 41(1), 29–44.

33.

Gurgul

, & Syrek

(2013). Testing of dependencies between stock returns and trading volume by high frequency data. Managing Global Transitions, 11(4), 353–373. Retrieved 11 February 2014, from http://www.fm-kp.si/zalozba/ISSN/1581-6311/11_353-373.pdf

34.

Gurgul

, Paweł

Majdosz

, & Roland

Mestel

. (2005). Joint dynamics of prices and trading volume on the Polish stock market. Managing Global Transitions, 3(2), 139–156.

35.

Habib

N.M.

(2011). Trade Volume and Returns in Emerging Stock Markets: An Empirical Study: The Egyptian Market. International Journal of Humanities and Social Science, 1(19), 302–312.

36.

Harris

(1987). Transaction data tests of the Mixture of Distributions Hypothesis. Journal of Financial and Quantitative Analysis, 22(2), 127–141.

37.

Harris

, & Gurel

(1986). Cross-Security Tests of Mixture of Distribution Hypothesis. The Journal of Financial and Quantitative Analysis, 21(1), 39–46.

38.

Hatekar

, Gupta

, Suryavanshi

, & Tiwari

(2013). Testing linear and non-linear Granger causality for stock price-volume relationship for S&P BSE SENSEX. Journal of Accounting and Finance, 53–65. Retrieved 11 February 2014, from http://scholar.google.co.in/scholar?q=Hatekar+2013+causality+stock+return+volume&btnG=&hl=en&as_sdt=0%2C5&as_vis=1

39.

Hatrick

, So

M.K.P.

, Chung

S.W.

, & Deng

(2011). Dynamic Relationship among Intraday Realized Volatility, Volume and Number of Trades. Asia-Pacific Financial Markets, 18(3), 291–317.

40.

Hiemstra

, & Jones

J.D.

(1994). Testing for linear and nonlinear Granger causality in the stock price-volume relation. The Journal of Finance, 49(5), 639–1664. Retrieved 15 March 2013, from http://www.tl.ntu.edu.tw/2012/asianfa2012/fullpaper/10461.pdf

41.

Jennings

R.H.

, & Fellingham

J.C.

(1981). An equilibrium model of asset trading with sequential information arrival. The Journal of Finance, 36(1), 143–161.

42.

Jennings

R.H.

, Starks

L.T.

, & Fellingham

J.C.

(1981). An Equilibrium Model of Asset Trading with Sequential Information Arrival. Journal of Finance, 36(1), 143–161.

43.

Jones

C.M.

, Kaul

, & Lipson

M.L.

(1994). Transactions, Volume and Volatility. The Review of Financial Studies, 7(4), 631–651.

44.

Kamath

R. R.

, Chakornpipat

, & Chatrath

(1998). Return Distribution and the Day-of-the-Week Effects in the Stock Exchange of Thailand. Journal of Economics and Finance, 22(2–3), 97–106.

45.

Karpoff

J.M.

(1987). The relation between price changes and trading volume: A survey. Journal of Financial and Quantitative Analysis, 22(1), 109–126.

46.

Kocagil

A.E.

, & Shachmurove

(1998). Return-volume dynamics in futures markets. The Journal of Futures Market, 18(4), 399–426.

47.

Konan

N.L.

(2007). An empirical study of the relation between stock return volatility and trading volume in the BRVM. African Journal of Business Management, 1(7), 176–184.

48.

Kumar

, Singh

, & Pandey

(2009). The Dynamic Relationship Between Price and Trading Volume: Evidence from Indian Stock Market. Ahmedabad: Indian Institute of Management.

49.

Lamoureux

C.G.

, & Lastrapes

W.D.

(1994). Endogenous trading volume and momentum in stock-return volatility. Journal of Business and Economic Statistics, 12(2), 253–260.

50.

Lee, & Swaminathan. (2000). Price Momentum and Trading Volume. The Journal of Finance, 55(5), 1017–1069.

51.

Lee

B.-S.

, & Rui

O.M.

(2002). The dynamic relationship between stock returns and trading volume: Domestic and cross-country evidence. Journal of Banking and Finance, 26(1), 51–78.

52.

Llorente

, Michaely, Saar

R.G.

, & Wang

(2002). Dynamic Volume-Return Relation of Individual Stocks. The Review of Financial Studies, 15(4), 1005–1047

53.

Lokman

, & Abdulnasser

(2005). Stock price-volume relation in East European stock markets. Emerging Markets Finance and Trade, 41(1), 29–45.

54.

Long

De. J.

(1990). Positive Feedback, Investment Strategies and Destabilizing Rational Speculation. The Journal of Finance, 45(2), 379–395.

55.

W.C.

, & Lin

F.J.

(2010). An Empirical Study of Volatility and Trading Volume Dynamics using High Frequency Data. The International Journal of Business and Finance Research, 4(3), 93–101.

56.

Y.C.

, Wei

Y.C.

, & Chang

C.W.

(2010). Stealth Trading, Price Manipulation and Investor Types in the Emerging Taiwan Equity Market. In the 23rd Annual Australasian Finance and Banking, Conference (AFBC), Sydney, Australia.

57.

Mahajan

, & Singh

(2007). Return-Trading Volume Linkage in the Indian Stock Market: Evidence from National Stock Exchange. ICFAI Journal of Applied Finance, 13(5), 57–68.

58.

Mahajan

, & Singh

(2008a). An empirical analysis of stock price-volume relationship in Indian stock market. Vision, 12(3), 1–13.

59.

Mahajan

, & Singh

(2008b). Trading volume and return volatility dynamics in Indian stock market. ICFAI Journal of Applied Finance, 14(2), 53–73.

60.

Mahajan

, & Singh

(2009). The empirical investigation of relationship between return, volume, and volatility dynamics in Indian stock market. Eurasian Journal of Business and Economics, 2(4), 113–137.

61.

Malliaris

A.G.

, & Urrutia

J. L.

(1992). The International Crash of October 1987: Causality Tests. Journal of Financial and Quantitative Analysis, 27(3), 353–364.

62.

Mcmillan

David

, & Speight

Alan

. (2002). Return-volume dynamics in UK futures. Applied Financial Economics, 12(10), 707–713.

63.

Medeiros

O.R.

, & Doornik

V.B.

(2008). The Empirical Relationship between Stock Returns, Return Volatility and Trading Volume in the Brazilian Stock Market. Brazilian Business Review, 5(1), 1–17.

64.

Mestel

, Gurgul

, & Majdosz. (2003). The empirical relationship between stock returns, return volatility and trading volume on the Austrian stock market. Austria: Institute of Banking and Finance, University of Graz.

65.

Mishra

(2004). The stock return-volume relation: Evidences from Indian IT industry. Retrieved 21 February 2013, from www.ssrn.com

66.

Mulherin

, & Gerety

(1988). Trading Volume on the NYSE during the Twentieth Century: A Daily and Hourly Analysis (Working Paper). USA: Securities and Exchange Commission.

67.

Nguyen, & Diagler. (2006). A return-volatility-volume analysis of futures contracts. Retrieved from 1 January 2013, from http://www.fma.org/

68.

Otavio

, & Bernardus

F.N.

(2006). The Empirical Relationship between Stock Returns, Return Volatility and Trading Volume in the Brazilian Stock Market (SSRN Working Paper). Retrieved 1 January 2013, from www.ssrn.com

69.

Pisedtasalasai

, & Gunasekarage

(2008). Causal and Dynamic Relationships among Stock Returns, Return Volatility and Trading Volume: Evidence from Emerging Markets in South-East Asia. Asia-Paciﬁc Finance Markets, 14(December), 277–297.

70.

Rajput

, Kakkar

, Batra

, & Gupta

(2012). Price discovery in Indian stock market: The case of S&P CNX Nifty index. Investment Management and Financial Innovations, 9(3), 120–129.

71.

Ramaprasad

, & Shigeyuki

(2004). Information flow between price change and trading volume in gold futures contracts. International Journal of Business and Economics, 3(1), 45–56.

72.

Reddy

S.Y.

(1997). Efficiency of Indian Stock Markets: An Empirical Analysis of Weak-Form EMH of the BSE (pp. 91–115). UTI Indian Capital Market Conference December.

73.

Smirlock

, & Starks

(1985). A further examination of stock price changes and transaction volume. The Journal of Financial Research, 8, 217–225.

74.

Stevenson

R.A.

, & Bear

R.M.

(1970). Commodity Futures: Trends or Random Walks. Journal of Finance, 25(1), 65–81.

75.

Sutcliffe

C.M.S.

(1993). Stock Index Futures: Theories and International, Evidence (1st edition). London: Chapman and Hall.

76.

Tambi

M.K.

(2005). An empirical study of return-volume relationship for Indian market. Retrieved 21 February 2013, from 29.3.20.41/eps/fin/papers/0504/0504013.pdf

77.

Tauchen

, & Pitts

(1983). The price variability-volume relationship on speculative markets. Econometrica, 51(2), 485–505.

78.

Wang

(1994). A model of competitive stock trading volume. Journal of Political Economy, 102(1), 127–168.