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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: 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: Allaudeen Hameed Publisher: ISBN: Category : Languages : en Pages :
Book Description
In this paper I show that the lead-lag pattern between large and small market value portfolio returns is consistent with differential variations in their expected return components. I find that the larger predictability of returns on the portfolio of small stocks may be due to a higher exposure of these firms to persistent (time-varying) latent factors. Additional evidence suggest that the asymmetric predictability cannot be fully explained by lagged price adjustments to common factor shocks: (i) lagged returns on large stocks do not have strong causal effect on returns on small stocks; (ii) trading volume is positively related to own and cross-autocorrelations in weekly portfolio returns; and (iii) significant cross- autocorrelation exists between current returns on large stocks and lagged returns on small stocks when trading volume is high.
Author: John Y. Campbell Publisher: ISBN: Category : Rate of return Languages : en Pages : 30
Book Description
This paper investigates the relationship between stock market trading volume and the autocorrelations of daily stock index returns. The paper finds that stock return autocorrelations tend to decline with trading volume. The paper explains this phenomenon using a model in which risk-averse "market makers" accommodate buying or selling pressure from "liquidity" or "non-informational" traders. Changing expected stock returns reward market makers for playing this role. The model implies that a stock price decline on a high-volume day is more likely than a stock price decline on a low-volume day to be associated with an increase in the expected stock return.
Author: John Y. Campbell Publisher: ISBN: Category : Languages : en Pages : 45
Book Description
This paper investigates the relationship between stock market trading volume and the autocorrelations of daily stock index returns. The paper finds that stock return autocorrelations tend to decline with trading volume. The paper explains this phenomenon using a model in which risk-averse quot;market makersquot; accommodate buying or selling pressure from quot;liquidityquot; or quot;non-informationalquot; traders. Changing expected stock returns reward market makers for playing this role. The model implies that a stock price decline on a high-volume day is more likely than a stock price decline on a low-volume day to be associated with an increase in the expected stock return.
Author: Bartosz Gebka Publisher: ISBN: Category : Languages : en Pages :
Book Description
We analyze the autocorrelation structure of returns and volatility of stocks listed in the single auction system on the Warsaw Stock Exchange during the period January 1996 - October 2000. First, we find that size- and volume-related cross-autocorrelation in portfolio returns exists even after accounting for the portfolio's own-autocorrelation. Second, we find that size and volume leadership are independent from each other. Third, our results indicate slower adjustment of the small (low volume) portfolios to market-wide information that differs for up and down markets. We also find evidence for volatility spillovers between portfolio returns.
Author: Deok Hyeon Lee Publisher: ISBN: Category : Languages : en Pages : 58
Book Description
Stocks with high trading volume outperform otherwise stocks for one week, but subsequently underperform at the longer horizon. We show that such time-varying predictability of trading volume is attributed to abnormal trading activity, which is not explained by past volume. Specifically, we find that the return forecasting power of abnormal trading activity is strongly positive up to five weeks ahead. In contrast, the predictive power of the expected trading activity is negative, and lasts for longer horizons. We further argue that behavioral biases and investors' attention induces abnormal trading activity, but its price impact is primarily related to behavioral biases. Overall evidence emphasizes the role of behavioral biases and investors' attention to explain trading volume.
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: Tarun Chordia
Author: Tarun Chordia
Author: Allaudeen Hameed
Author: John Y. Campbell
Author: John Y. Campbell
Author: Rochelle L. Antoniewicz
Author: John Y. Campbell
Author: Rochelle L. Antoniewicz
Author: Bartosz Gebka
Author: Deok Hyeon Lee
Author: William A. Brock