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Author: Vicentiu Covrig Publisher: ISBN: Category : Languages : en Pages : 31
Book Description
This study investigates whether the widely documented daily correlated trading volume of stocks is driven by individual investor trading, institutional trading, or both. We find that at least 95 percent of NYSE and AMEX stocks exhibit statistically significant, positive serial correlation. Volume autocorrelation decreases with the level of institutional ownership of a stock. We also show that the rate of arrivals of new information to the market contributes to the clustering of the trades. When there is high information flow to the market, institutional trading generates a more pronounces effect on volume autocorrelation than individual investor trading. Our results are broadly consistent with the predictions of trading volume patterns suggested by most theoretical models of stock trading and by empirical research on investor trading.
Author: Vicentiu Covrig Publisher: ISBN: Category : Languages : en Pages : 31
Book Description
This study investigates whether the widely documented daily correlated trading volume of stocks is driven by individual investor trading, institutional trading, or both. We find that at least 95 percent of NYSE and AMEX stocks exhibit statistically significant, positive serial correlation. Volume autocorrelation decreases with the level of institutional ownership of a stock. We also show that the rate of arrivals of new information to the market contributes to the clustering of the trades. When there is high information flow to the market, institutional trading generates a more pronounces effect on volume autocorrelation than individual investor trading. Our results are broadly consistent with the predictions of trading volume patterns suggested by most theoretical models of stock trading and by empirical research on investor trading.
Author: Benjamin M. Blau Publisher: ISBN: Category : Languages : en Pages : 36
Book Description
This study finds evidence of autocorrelation in daily short-sale volume. The degree of autocorrelation in short volume, however, is not driven by illiquid stocks or stocks that face short-sale constraints. Contrary to prior research that suggests that autocorrelation in total trade volume is explained by the flow of information into prices, our tests show that the information contained in short sales is decreasing in the level of autocorrelation. Further, we do not find that short sellers engage in stealth trading strategies indicating that stealth trading activity is not a necessary condition for the presence of autocorrelation in trading volume.
Author: Tarun Chordia Publisher: ISBN: Category : Languages : en Pages :
Book Description
This paper finds that trading volume is a significant determinant of the lead-lag patterns observed in stock returns. Daily and weekly returns on high volume portfolios lead returns on low volume portfolios, controlling for firm size. Nonsynchronous trading or low volume portfolio autocorrelations cannot explain these findings. These patterns arise because returns on low volume portfolios respond more slowly to information in market returns. The speed of adjustment of individual stocks confirms these findings. Overall, the results indicate that differential speed of adjustment to information is a significant source of the cross-autocorrelation patterns in short-horizon stock returns.
Author: Horace Chueh Publisher: ISBN: Category : Languages : en Pages : 25
Book Description
Trading volume conveys critical information on future price changes, which are of interests to all market participants. This paper inspects trading volume with the intraday transaction data of the TAIEX futures trade on the Taiwan Futures Exchange. The results support the theory of Llorente et al. (2002). Trading days associated with a high degree of information asymmetry exhibit more return continuation on high-volume transactions and those associated with a low degree of information asymmetry demonstrate more return reversals on high-volume transactions. Time-varying analyses show that high-volume transaction creates more return continuation around the opening period of a trading day, coupled with a high degree of informed trading.
Author: William A. Brock Publisher: ISBN: Category : Stochastic processes Languages : en Pages : 46
Book Description
This paper seeks to develop a structural model that lets data on asset returns and trading volume speak to whether volatility autocorrelation comes from the fundamental that the trading process is pricing or, is caused by the trading process itself. Returns and volume data argue, in the context of our model, that persistent volatility is caused by traders experimenting with different beliefs based upon past profit experience and their estimates of future profit experience. A major theme of our paper is to introduce adaptive agents in the spirit of Sargent (1993) but have them adapt their strategies on a time scale that is slower than the time scale on which the trading process takes place. This will lead to positive autocorrelation in volatility and volume on the time scale of the trading process which generates returns and volume data. Positive autocorrelation of volatility and volume is caused by persistence of strategy patterns that are associated with high volatility and high volume. Thee following features seen in the data: (i) The autocorrelation function of a measure of volatility such as squared returns or absolute value of returns is positive with a slowly decaying tail. (ii) The autocorrelation function of a measure of trading activity such as volume or turnover is positive with a slowly decaying tail. (iii) The cross correlation function of a measure of volatility such as squared returns is about zero for squared returns with past and future volumes and is positive for squared returns with current volumes. (iv) Abrupt changes in prices and returns occur which are hard to attach to 'news.' The last feature is obtained by a version of the model where the Law of Large Numbers fails in the large economy limit
Author: Ehab Abdel-Tawab Yamani Publisher: ISBN: Category : Languages : en Pages : 19
Book Description
This paper investigates the relation between stock returns and trading volume (as measured by turnover) in a small emerging market, i.e., the Egyptian Securities Exchange (ESE). We are interested in examining the power of stock trading volume in predicting future return. To this end, we use a version of GARCH model and Granger causality test to measure the correlation and causality relation between trading volume and stock return, respectively. The results show that trading volume data plays no role in predicting stock return autocorrelation.
Author: Hua Cheng Publisher: ISBN: Category : Languages : en Pages : 40
Book Description
We develop a dynamic model in which traders have differential information about the true value of the risky asset and trade the risky asset with proportional transaction costs. We show that without additional assumption, trading volume can not totally remove the noise in the pricing equation. However, because trading volume increases in the absolute value of noisy per capita supply change, it provides useful information on the asset fundamental value which cannot be inferred from the equilibrium price.We further investigate the relation between trading volume, price autocorrelation, return volatility and proportional transaction costs. Firstly, trading volume decreases in proportional transaction costs and the influence of proportional transaction costs decreases at the margin. Secondly, price autocorrelation can be generated by proportional transaction costs: under no transaction costs, the equilibrium prices at date 1 and 2 are not correlated; however under proportional transaction costs, they are correlated - the higher (lower) the equilibrium price at date 1, the lower (higher) the equilibrium price at date 2. Thirdly, we show that return volatility may be increasing in proportional transaction costs, which is contrary to Stiglitz 1989, Summers amp; Summers 1989's reasoning but is consistent with Umlauf 1993 and Jones amp; Seguin 1997's empirical results.
Author: William A. Brock Publisher: ISBN: Category : Languages : en Pages : 51
Book Description
This paper seeks to develop a structural model that lets data on asset returns and trading volume speak to whether volatility autocorrelation comes from the fundamental that the trading process is pricing or, is caused by the trading process itself. Returns and volume data argue, in the context of our model, that persistent volatility is caused by traders experimenting with different beliefs based upon past profit experience and their estimates of future profit experience. A major theme of our paper is to introduce adaptive agents in the spirit of Sargent (1993) but have them adapt their strategies on a time scale that is slower than the time scale on which the trading process takes place. This will lead to positive autocorrelation in volatility and volume on the time scale of the trading process which generates returns and volume data. Positive autocorrelation of volatility and volume is caused by persistence of strategy patterns that are associated with high volatility and high volume. Thee following features seen in the data: (i) The autocorrelation function of a measure of volatility such as squared returns or absolute value of returns is positive with a slowly decaying tail. (ii) The autocorrelation function of a measure of trading activity such as volume or turnover is positive with a slowly decaying tail. (iii) The cross correlation function of a measure of volatility such as squared returns is about zero for squared returns with past and future volumes and is positive for squared returns with current volumes. (iv) Abrupt changes in prices and returns occur which are hard to attach to 'news.' The last feature is obtained by a version of the model where the Law of Large Numbers fails in the large economy limit.
Author: Vicentiu Covrig
Author: Vicentiu Covrig
Author: Benjamin M. Blau
Author: 
Author: Tarun Chordia
Author: Horace Chueh
Author: William A. Brock
Author: Ehab Abdel-Tawab Yamani
Author: Hua Cheng
Author: William A. Brock
Author: Aaron Gilbert