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Cited by 274

The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.

Delayed Reaction to Good News and the Cross‐Autocorrelation of Portfolio Returns

MCQUEEN, GRANT, PINEGAR, MICHAEL, THORLEY, STEVEN·1996·The Journal of Finance

Abstract

We document a directional asymmetry in the small stock concurrent and lagged response to large stock movements. When returns on large stocks are negative, the concurrent beta for small stocks is high, but the lagged beta is insignificant. When returns on large stocks are positive, small stocks have small concurrent betas and very significant lagged betas. That is, the cross‐autocorrelation puzzle documented by Lo and MacKinlay (1990a) is associated with a slow response by some small stocks to good, but not to bad, common news. Time series portfolio tests and cross‐sectional tests of the delay for individual securities suggest that existing explanations of the cross‐autocorrelation puzzle based on data mismeasurement, minor market imperfections, or time‐varying risk premiums fail to capture the directional asymmetry in the data.

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Crossref · 10.1111/j.1540-6261.1996.tb02711.x

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Metadata

Authors
MCQUEEN, G., PINEGAR, M., & THORLEY, S.
Venue
The Journal of Finance
Publisher
Wiley
Published
1996-07
Volume
51
Issue
3
Pages
889-919
ISSN
  • 0022-1082 (ISSN-L)
  • 1540-6261
OpenAlex
W1963881500
Cited by
274
References
39
FWCI
13.20
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Delayed Reaction to Good News and the Cross‐Autocorrelation of Portfolio Returns. https://app.cochat.ai/papers/10.1111/j.1540-6261.1996.tb02711.x
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MCQUEEN, G., PINEGAR, M., & THORLEY, S. (1996). Delayed Reaction to Good News and the Cross‐Autocorrelation of Portfolio Returns. The Journal of Finance. https://doi.org/10.1111/j.1540-6261.1996.tb02711.x

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.