Cross-autocorrelations in Security Returns and Their Relationships with Seasonal Patterns in Security Returns and Firm-specific Forecasting Variables PDF Download
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Author: Eric James Higgins
Publisher:
ISBN:
Category : Securities
Languages : en
Pages : 536
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Book Description
Author: Eric James Higgins
Publisher:
ISBN:
Category : Securities
Languages : en
Pages : 536
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Book Description
Author:
Publisher:
ISBN:
Category : Dissertation abstracts
Languages : en
Pages : 872
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Author:
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 584
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Book Description
Abstracts of dissertations available on microfilm or as xerographic reproductions.
Author: David Kenji Heike
Publisher:
ISBN:
Category : Securities
Languages : en
Pages : 234
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Author: Terry Richardson
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Prior studies find evidence of asymmetric size-based portfolio return cross-autocorrelations where lagged large-firm returns lead current small-firm returns. However, Boudoukh, Richardson, and Whitelaw (1994) question whether this economic relationship is independent of the impact of portfolio return autocorrelation. We formally test for this independence using size-based portfolios of New York and American Stock Exchange securities and, separately, portfolios of NASDAQ securities. Results from Granger (1969) causality regressions indicate that, across all markets, lagged large-firm returns predict current small-firm returns, even after controlling for autocorrelation in small-firm returns. These cross-autocorrelation patterns are stronger for NASDAQ securities.
Author: Xiaojun He
Publisher:
ISBN:
Category :
Languages : en
Pages : 36
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Book Description
This paper develops a common-factor model to investigate relationships between security returns/return volatility and trading volume. The model generalizes Tauchen and Pitts' (1983) MDH model by capturing possible interactions among securities. In our model, both price changes and trading volume are governed by three kinds of mutually independent variables: common factor variables, latent information variables and idiosyncratic variables. Despite its similarity to Hasbrouck and Seppi's (2001) model in terms of the form, the model extraordinarily allows us to identify the cause of interactions among securities by decomposing factor loadings into constant and random components. Three key implications are reached from our model. First, common factor structures in returns and trading volume stem from information flows. Second, returns' common factors are not related to trading volume's common factors. This implication directly opposes Hasbrouck and Seppi's (2001) assumption. Finally, cross-firm variations of returns and volume respectively rely on underlying latent information flows. The positive relation between return volatility and volume also results only from underlying latent information flows. Thus, common factor structures in returns and trading volume have no additional explanatory power in cross-firm variations and the positive return volatility-volume relationship. We fit the model for intraday data of Dow Jones 30 stocks using the EM algorithm. The results support specifications of our model. The empirical results demonstrate 3-factor structures in returns and trading volume, respectively. All 30 stocks in our sample are governed by at least one common factor. This fact implies that our model outperforms Tauchen and Pitts' (1983) model because their model is a special case of our model without the presence of common factors. We also show that after controlling the effect of information flows, persistence in return variance disappears.
Author:
Publisher: Springer
ISBN: 1349588024
Category : Law
Languages : en
Pages : 7493
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Book Description
The award-winning The New Palgrave Dictionary of Economics, 2nd edition is now available as a dynamic online resource. Consisting of over 1,900 articles written by leading figures in the field including Nobel prize winners, this is the definitive scholarly reference work for a new generation of economists. Regularly updated! This product is a subscription based product.
Author: Tarun Chordia
Publisher:
ISBN:
Category :
Languages : en
Pages : 32
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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: Richard J. Murdock
Publisher:
ISBN:
Category : Business enterprises
Languages : en
Pages : 294
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Book Description
Author: Vincent Bogousslavsky
Publisher:
ISBN:
Category :
Languages : en
Pages : 45
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Book Description
A model of infrequent rebalancing can explain specific predictability patterns in the time-series and cross-section of stock returns. First, infrequent rebalancing produces return autocorrelations that are consistent with empirical evidence from intraday returns and new evidence from daily returns. Autocorrelations can switch sign and become positive at the rebalancing horizon. Second, the cross-sectional variance in expected returns is larger when more traders rebalance. This effect generates seasonality in the cross-section of stock returns, which can help explain available empirical evidence.
