How to Invest in Penny Stocks: When Too Much Trading Volume is a Bad Thing
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The analysis showed that the recent news of trading volume can be used to improve the prediction of price momentum and trading volume lee price volatility. This study also found the evidence of leverage and asymmetric effect of trading volume in stock market and indicated that bad news generate more impact on the volatility of the stock price in the market. Pricing of securities depends on volatility of each asset. Therefore, price changes indicate the average reaction of investors to news.
The arrival of new information makes investors to adapt their expectations and this is the main cause for price and return changes. Trading volume and volatility are indicators of the current stock market activity on one hand and a potential source of information for the future price momentum and trading volume lee of stock market on the other hand. Numerous papers have documented the fact that high stock market volume is associated with volatile returns.
However, many theoretical and empirical studies are designed to work with the conditional variance in developed markets Dimson and Marsh, ; McMillan et al. Various studies reported that there are significant relationships between volume and stock price price momentum and trading volume lee and volatility. For example, Saatcioglu and Starks found that volume led stock prices changes in four out of the six emerging markets.
Recently, several authors have investigated the volatility of stock market by applying econometric models and suggested that, no single model is superior Akgiray, ; Pagan and Schwert, Dimson and Marsh examined various technical methods of predicting the volatility of UK stock market returns and find that exponential smoothening and price momentum and trading volume lee model performed.
Further we also analysis the contemporaneous relationship between stock price volatility and trading volume. Also a few attempts were made to model the most prominent features of the time series of Nasdaq index such as volatility clustering, excess kurtosis and fat tailed by applying the most popular techniques proposed by Engle The random walk model is the simplest possible models, where the Ordinary Least Square OLS method are constructed on the assumption of constant variance.
As per, efficient market hypothesis the competing market participants reflect information instantly hence are useless in predicting future prices. An unexpected increase or decrease in returns at time t will generate an increase in the expected variability in the next period.
In particular, volatility forecast are increased following a large positive and negative index return, the GARCH specification that capturing price momentum and trading volume lee well-documented volatility clustering evident in financial returns date Engle, In TGARCH model, it has been observed that positive and negative shocks of equal magnitude have a different impact on stock market volatility, which may be attributed to a leverage effect Black, In the same sense, negative shocks are followed by higher volatility than positive shocks of the same magnitude Engle and Ng, The main target of this model is to capture asymmetric in terms of negative and positive shocks and adds multiplicative dummy variable to check whether there is statistically significant different when shocks are negative.
So good news and bad news have a different impact. The Exponential GARCH model specifies conditional variance in logarithmic form, which means that there is no need to impose estimation constraints in order to avoid negative variance Nelson The mean and variance equation for this model is given by:. The exponential nature of EGARCH ensures that the conditional variance is always positive even if the parameter values are negative; thus there is no need for parameter restrictions to impose non-negativity.
Smirlock and Starks found that the return-volume relation is asymmetric and later, Smirlock and Starks found a strong positive lagged relationship between volume and absolute price changes using individual stock data.
Bekaert and Wu not only support this finding but also suggest that negative shocks generate a greater response in volatility than positive shocks of an equal magnitude, evidence of the speed of information transmission in markets.
Thus, the findings of past studies are strong indications of information content of volatility on the markets, which could be used by investors to earn abnormal profit. Ratner and Leal examined the Latin American and Asian financial markets and find a positive contemporaneous relation between return and volume in these countries except India. At the same time they observed that there exists a bi-directional causal relation between return and volume.
In summary, the return and volume are strongly related contemporaneously but there is little evidence that either can be used to predict the other. Price momentum and trading volume lee Medeiros and Doornik investigated the empirical relationship between stock returns, return volatility and trading volume using data from the Brazilian stock market.
The study found out there is a contemporaneous and dynamic relationship between return volatility and trading volume and return volatility contains information about upcoming trading volumes. Atmeh and Dobbs investigated the performance of moving average trading rules in the Jordanian stock market and found that technical trading rules can help to predict market movements.
Al-Khouri and Ajlouni reported that the price-limit technique was effective in reducing the volatility in the Amman stock exchange. Floros and Vougas used GARCH and GMM method to investigate the relationship between trading volume and returns in Greek stock index futures market and found that trading volume was used as the indicator of prices. The basic descriptive analysis of the time series of stock returns and trading volume is shown in Table 1.
All returns are calculated as the first difference of the log of the daily closing price. Daily trading volume and stock return have positive kurtosis and high JB statistics that implies that the distribution is skewed to the right and they are leptokurtic heavily tailed and sharp peakedi.
The Jarque-Bera statistic test indicates price momentum and trading volume lee the null price momentum and trading volume lee of normality is rejected and shows that all the series exhibit non-normality and indicates the presence of Heteroscedasticity. The study here employs the unit root test to examine the time series properties of concerned variables.
Unit root test describes whether a series is stationary or non-stationary. ADF test is used to measure the stationarity of time series data which in turn tells whether regression can be done on the data or not. The output is presented in the Table 2. So, the null hypothesis is rejected and the data is found to be stationary. We investigate that weather trading volume has an explanatory power for Indian stock market by fitting GARCH 1, 1 model with daily volume included in the conditional variance equation.
Systematic variations in trading volume are assumed to be caused only by the arrival of new information. AIC and SIC criteria used in the study indicating lower for the regression which is quite reasonable and fit for our model.
Further Durbin-Watson value is 2 suggests autocorrelation or specification errors. Since the Durbin-Watson statistic is greater than 2, the error terms are not auto correlated indicating that the statistical model is fit and appropriate.
It is very often observed that downward movement of the markets is followed by higher volatilities than upward movement of the same magnitude. If the bad news has a greater impact on volatilities than good news, a leverage effect exists. ARCH model helps to explain the volatility of spot market when some degree price momentum and trading volume lee is present in the data. TARCH model takes the leverage effect into account. The presence of leverage effect is seen in Table 4 which implies that every price changes are responding asymmetrically to the positive and negative news in the market.
The analysis shows that trading volume is associated with an increase in stock return price momentum and trading volume lee. Good news therefore induces more trading volume than bad news. So trading volume increased the stock return and decrease expected volatility in the market. This supports a positive correlation between trading volume and predictable volatility of stock returns.
The analysis shows that the PARCH model which exhibits a low power effects but strong leverage effects in the market. This paper specifically tested the hypothesis of variability in volatility, which implies that volatility is greater when stocks price are moving downwards than upwards. The study found that the recent news has an impact on the volatility of the trading volume.
Also, the past news coefficient is price momentum and trading volume lee insignificant and suggests that old news is not having influencing the trading volume volatility. So it is evident from the study that systematic variations in trading volume are assumed to be caused only by the arrival of new information. This implied that daily new information in market may have significant impact on price volatility.
So the study concludes that bad news generate more impact on volatility of the stock return and trading volume. One explanation may be that normally investors have a higher aversion to downside risk, so they react faster to bad news.
Ravichandran and Sanjoy Bose. Similar Articles in this Journal. Search in Google Scholar. How to cite this article: Ravichandran and Sanjoy Bose, Research Journal of Business Management, 6: September 12, ; Accepted: November 27, ; Published: Conditional heteroskedasticity in time series of stock returns: Narrow price limit and stock price volatility: Empirical evidence from Amman stock exchange.
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