Using the study conducted for a period of 5 years from January 2005 to January 2010 (Tripathy, 2010) investigates the relationship between stock trade volume and volatility of stock returns. The objective of the study is achieved by utilizing financial tools such as ARCH, GARCH, EGARCH, TARCH, PGARCH, and Component ARCH models. The investigation demonstrates that the forecast of stock return volatility can be enhanced by the utilization of the current news regarding the volume of trade. Also (Tripathy, 2010) provides evidence that there is an existence of leverage and asymmetric effect of trading volume present in the stock market, and is established that negative news has more impact on the volatility of stock returns.
A huge positive relationship between stock return and volume of trade was found in the study conducted by (Attari, 2012) providing the fact that a rise in volume comes along with a rising market and applies the other way around as well. This statement portrays that the future return of stocks is heavily influenced by the information on volume traded. The study, (Attari, 2012) says that volume which is influenced by market data, prompts value changes and information that provide positive information like an increase in capital gain will lead to an increase in the trade volume, thus empowering a higher rate of transactions of the stock. Past information on stock price and trading volume can be used by the likes of speculators and hedgers to predict future patterns in stock prices and use the same for trading decisions, (Attari, 2012) state that there.
Save your time!
We can take care of your essay
- Proper editing and formatting
- Free revision, title page, and bibliography
- Flexible prices and money-back guarantee
The Granger causality test used by (Nandan, 2016) and was applied to the data from 2008-09 to 2014-15 taken on a quarterly basis of the top ten companies in the National Stock Exchange to determine the relationship between stock returns, stock price volatility, and trade volume of the stock of the companies. There exists a bi-directional relationship between trading volume and stock returns i.e. both variables are found to reinforce each other as stated (Nandan, 2016). In another stock bi-directional relationship between stock return and volatility was established. It was also found in the study that two stocks showed a uni-directional relationship, meaning that the return influenced trading volume but trading volume did not impact return. It is implied (Nandan, 2016) that an indication of noise trading model of interaction is present between stock returns and trading volume.
The study (Iqbal, 2015) recommends that at the market level there is a sure contemporary relationship between returns and trade volume however for stocks the study proposes a positive contemporaneous relationship in two stocks and a negative contemporaneous relationship in three stocks among volume and returns. (Iqbal, 2015) Establishes that there is no bi-directional relationship between the two variables, volume, and returns which is supported by the evidence from the findings of the study that past volume does not cause returns but past returns do cause volume. Volume is not capable of removing the ARCH effect proposes (Iqbal, 2015) and also mentioned that a significant relationship is present between the volume of trade and stock volatility. Even by introducing trade volume as an advisory variable in the GARCH model, it is stated that the ARCH effect cannot be reduced and suggests the traders need not find the trade volume as a valuable informative variable.
Monthly data time series is used to study the relationship between stock returns and trading volume (Kant, 2011) for a period of nine years from January 2002 to December 2010 on a number of 347 stocks from the stock market with the help of descriptive statistics mean, median, maximum, minimum, standard deviation, skewness, kurtosis, jarque-bera, probability, sum, sum sq. deviation, and granger causality tests. The research shows that the two variables are dependent and so the information on one variable can be used to predict the patterns of the other variable. Using the granger causality method, (Kant, 2011) establishes that there is a co-integration between the variables thus indicating that there is a presence of a long-run relationship between the two. In the event that there is any linkage between both factors, the emergencies can be turned away either by overseeing trade volume or using indigenous policies to stabilize the stock market, states (Kant, 2011).
The contemporaneous and asymmetric relation between price and volume are examined for 50 Indian stocks by (Kumar, 2010). The dynamic relationship between the two variables is also examined in the study using various tools such as VAR, Granger causality, variance decomposition, and impulse response function. Evidence of a positive contemporaneous correlation between price changes and trading volume was found to be present in the Indian stock markets. The results provide evidence of the existence of a positive and asymmetric relationship between the two variables and the results from VAR and Granger causality establish a bi-directional relation between volume and returns, states (Kumar, 2010).
Stuck on your essay?
