The asymmetric price-volume relation revisited: evidence from Qatar
| Date | 08 May 2018 |
| Pages | 193-219 |
| DOI | https://doi.org/10.1108/JABS-11-2015-0194 |
| Published date | 08 May 2018 |
| Author | Walid M.A. Ahmed |
| Subject Matter | Strategy,International business |
The asymmetric price-volume relation
revisited: evidence from Qatar
Walid M.A. Ahmed
Abstract
Purpose –This study aims to revisit the stock price–volumerelations, providing new evidence from the
emerging market of Qatar. In particular, three main issues are examinedusing both aggregate market-
and sector-level data. First, the return–volume relation and whether or not this relation is asymmetric.
Second, the common characteristics of return volatility; and third, the nature of the relation between
tradingvolume and return volatility.
Design/methodology/approach –The study uses the OLS and VAR modelingapproaches to examine
the contemporaneous and dynamic (causal) relations between index returns and trading volume,
respectively, while an EGARCH-X(1,1) model is used to analyze the volatility–volume relation. The data
set comprises dailyindex observations and the corresponding tradingvolumes for the entire market and
the individual seven sectorsof the Qatar Exchange (i.e. banks and financial services, consumer goods
and services,industrials, insurance, real estate, telecommunicationsand transportation).
Findings –The empirical analysis reports evidence of a positive contemporaneous return–volume
relation in all sectors barringtransportation and insurance. This relation appears to be asymmetricfor all
sectors. For the market and almost all sectors, there is no significant causality between returns and
volume. By and large, these findings lend support for the implications of the mixture of distributions
hypothesis (MDH). Lastly,the information content of lagged volume seems to havean important role in
predictingthe future dynamics of return volatilityin all sectors, with the industrialsbeing the exception.
Practical implications –The findings provide important implications for portfolio managers and
investors, given that the volume of transactions is generally found to be informative about the price
movement of sector indices. Specifically, tracking the behavior of trading volume over time can give a
broad portrayal of the future direction of market prices and volatility of equity, thereby enriching the
informationset available to investors for decision-making.
Originality/value –Based on both market- and sector-level data from the emerging stock market of
Qatar, this study attempts to fill an important void in the literature by examining the return–volume and
volatility–volumelinkages.
Keywords Causality, EGARCH, Asymmetric effects, Price volatility, Qatar Stock Exchange,
Trading volume
Paper type Research paper
1. Introduction
The linkage between stock prices and trading volume, whether at the firm, industry or
market level, has immensely absorbed academic and professional communities for
decades. One of the primary reasons for this resurgent interest is the key role that both
variables have when it comes to asset valuation and allocation. Besides, the dynamic
behavior of prices and volume provide market participants with important clues about
market microstructure and investorinteractions.
More specifically, trading volume, viewed as a proxy variable for the flow of information into
the market, can serve as a useful function in ameliorating the prediction of future returns
and return volatility which, in turn, constitute the foundation stones of risk management,
equity valuation and portfolioallocation and rebalancing decisions. Further, volumedata are
Walid M.A. Ahmed is
Associate Professor at the
Department of Business
Administration, Ain Shams
University, Cairo, Egypt.
Received 21 November 2015
Revised 8 June 2016
Accepted 10 September 2016
DOI 10.1108/JABS-11-2015-0194 VOL. 12 NO. 2 2018, pp. 193-219, ©Emerald Publishing Limited, ISSN 1558-7894 jJOURNAL OF ASIA BUSINESS STUDIES jPAGE 193
broadly used to identify the status quo of the market and to help portray its behavior trend.
Liu et al. (2016) document that trading volume is a major determinant of bid-ask spreads.
As pointed out by Ciner (2002) and Kim et al. (2005), more knowledge on market
microstructure can be acquired through examining the joint dynamics of stock prices and
volume than studying only the univariatedynamics of prices.
Indeed, the information contained in stock prices typically reflects a vivid picture of many
aspects of firms’ profiles. In general, investors and fund managers draw upon daily stock
price data to capture some vital corporate fundamentals such as earnings/price ratio, book/
market ratio and dividend yield, thereby making sensible investment decisions. Stock
market prices can also be indicative of the economic prospects of a country. Moreover,
monetary authorities and policymakers incessantly put the direction and magnitude of stock
price changes under the microscope, given that such changes may influence the
economy’s fundamentalsin subtle and complex ways over possiblylong periods of time.
The relevant research domain provides some theoretical interpretations for the observed
price–volume linkage, with the mixture of distributions hypothesis (MDH,henceforth) (Clark,
1973;Harris, 1986;Andersen,1996;Liesenfeld, 2001) and the sequential information arrival
hypothesis (SIAH, henceforth) (Copeland, 1976;Morse, 1980;Jennings and Barry, 1983;
Brooks, 1998) being the most commonlycited models accounting for such a relation.
The MDH posits that asset prices and trading activity (volume) tend to be positively related
because they are together reliant on a common underlying driving factor, which is thought
to be the rate of information flow. The joint distribution of trading volume and price is
assumed to be bivariate normal conditional on the same underlying latent news arrival.
Owing to the fact that the random arrival of new pieces of information is unobservable, data
on trading volume levels areused as a proxy for it. According to the MDH, all market traders
react simultaneously to new information, causing the transition of prices toward new
equilibria to occur instantly. This implies that the information content of past observations of
trading volume has no significant predictive power for explaining asset price movements,
and vice versa, as these two variables exhibit perfect synchronicity in their adjustment to
new unexpected information signals(Bollerslev and Jubinski, 1999;Darrat et al., 2003).
Within the framework of SIAH, on the other hand, shifts to new equilibria are not of such an
instantaneous nature. That is, the dissemination of new pieces of information into the hands
of market traders takes place sequentially rather than synchronously, and as a result, the
forces of demand and supply interpret and respond to these information signals at various
speeds. There exist intermediate equilibrium processes culminating with a general
condition of market equilibrium. Hence,the overall market equilibrium is supposed to evolve
through a series of successive individual equilibria. Under this scenario, the information
contained in past values of trading volume may have the ability to improve the prediction of
price changes, and vice versa. This suggests a positive causal relationship running from
either trading volume or price changes to the othervariable.
In this regard, the implications of both MDH and SIAH appear anomalous to the efficient
market hypothesis (EMH, henceforth)(Fama,1970, 1991). The weak form of the EMH posits
that all relevant past information is fully impounded in the price of financial assets and,
consequently, using historical asset prices (or volume) to draw inference on future trends
would prove futile. As the arrival of new information in the market tends to be erratic and
irregular, asset prices react in a random fashion, and investors would not be able to earn
excess returns by way of technical analysis tools. Hence, in Granger causality jargon,
informational efficiency existsif historical observations of volume (prices) cannot be used to
forecast future movements in prices (volume), which implies an independent relationship
between the two variables (Granger, 1988;He et al., 2014). In fact, there exists a growing
stream of empirical studies that document that the price–volume nexus across different
market settings is consistent with either the MDH or SIAH implications, thus casting doubt
PAGE 194 jJOURNAL OF ASIA BUSINESS STUDIES jVOL. 12 NO. 2 2018
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