Information-Based Trading and Autocorrelation in Individual Stock Returns PDF Download
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Author: Xiangkang Yin Publisher: ISBN: Category : Languages : en Pages : 69
Book Description
Applying a recently developed approach, the paper estimates the daily arrival rates of buy and sell orders originated from different trading motives for each stock in a sample of NYSE-listed companies. Based on these arrival rates, it shows that stock return tends to continue on consecutive days when privately-informed trading prevails, leading to positive return autocorrelation. But return is more likely to reverse itself on days with continuous trading on dispersion in beliefs, leading return autocorrelation to be more negative. Contrarian trading strategies conditional on daily measures of investment disagreement can yield economically and statistically significant excess returns.
Author: Xiangkang Yin Publisher: ISBN: Category : Languages : en Pages : 69
Book Description
Applying a recently developed approach, the paper estimates the daily arrival rates of buy and sell orders originated from different trading motives for each stock in a sample of NYSE-listed companies. Based on these arrival rates, it shows that stock return tends to continue on consecutive days when privately-informed trading prevails, leading to positive return autocorrelation. But return is more likely to reverse itself on days with continuous trading on dispersion in beliefs, leading return autocorrelation to be more negative. Contrarian trading strategies conditional on daily measures of investment disagreement can yield economically and statistically significant excess 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: 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: Robert A. Jarrow Publisher: ISBN: Category : Derivative securities Languages : en Pages : 472
Book Description
Written by a number of authors, this text is aimed at market practitioners and applies the latest stochastic volatility research findings to the analysis of stock prices. It includes commentary and analysis based on real-life situations.
Author: Michael D. McKenzie Publisher: ISBN: Category : Languages : en Pages :
Book Description
This paper investigates whether return volatility, trading volume, return asymmetry, business cycles and day-of-the-week are potential determinants of conditional autocorrelation in stock returns. The primary focus is on the role of feedback trading and the interplay of return volatility. We present empirical evidence using conditional autocorrelation estimates generated from multivariate generalised autoregressive conditional heteroscedasticity (M-GARCH) models for individual U.S. stock and index data. In addition to return volatility, we find that trading volume and market returns are important in explaining the time-varying patterns of return autocorrelation.
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: Doron Avramov Publisher: ISBN: Category : Languages : en Pages : 41
Book Description
This paper documents a strong relationship between short-run reversals and stock return illiquidity, even after controlling for trading volume. The largest reversals and the potential contrarian trading strategy profits occur in the high turnover, low liquidity stocks, as the price pressures caused by non-informational demands for immediacy are accommodated. Thus, the high frequency negative autocorrelations are more likely to result from stresses in the market for liquidity. The contrarian trading strategy profits are smaller than the likely transactions costs because the high turnover, low liquidity stocks face large transaction and market impact costs. This lack of profitability and the fact that the overall findings are consistent with rational equilibrium paradigms suggest that the violation of the efficient market hypothesis due to short-term reversals is not so egregious after all.
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: Xiangkang Yin Publisher: ISBN: Category : Languages : en Pages : 61
Book Description
This paper investigates the dynamic relation between information-based trading of a stock and its daily return and risk. It develops a theoretical model to motivate the regression specifications for empirical analysis. Based on two samples of stocks, we demonstrate that the expected trading imbalance determines stock daily return while the expected total trades determine volatility. Trade imbalance arisen from private information plays a dominant role in determining return while trades due to disputable public information are the dominant contributor to the risk. Public-information trading is further identified to be associated with the contemporaneous idiosyncratic risk rather than systematic risk.
Author: Xiangkang Yin
Author: Xiangkang Yin
Author: Tarun Chordia
Author: Vicentiu Covrig
Author: Rochelle L. Antoniewicz
Author: Robert A. Jarrow
Author: Michael D. McKenzie
Author: William A. Brock
Author: Doron Avramov
Author: John Y. Campbell
Author: Xiangkang Yin