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Cited by 675

The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.

Decoding Inside Information

Open access
COHEN, LAUREN, MALLOY, CHRISTOPHER, POMORSKI, LUKASZ·2012·The Journal of Finance

Abstract

Exploiting the fact that insiders trade for a variety of reasons, we show that there is predictable, identifiable “routine” insider trading that is not informative about firms’ futures. A portfolio strategy that focuses solely on the remaining “opportunistic” traders yields value‐weighted abnormal returns of 82 basis points per month, while abnormal returns associated with routine traders are essentially zero. The most informed opportunistic traders are local, nonexecutive insiders from geographically concentrated, poorly governed firms. Opportunistic traders are significantly more likely to have SEC enforcement action taken against them, and reduce trading following waves of SEC insider trading enforcement.

Overview

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Integrity signals

Registry

Crossref · 10.1111/j.1540-6261.2012.01740.x

Cites

23 of 23 references verified0 citation errors · 0 not checkable

Statistics

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Citations

Cites

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Cited by

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Metadata

Authors
COHEN, L., MALLOY, C., & POMORSKI, L.
Venue
The Journal of Finance
Publisher
Wiley
Published
2012-05-21
Volume
67
Issue
3
Pages
1009-1043
ISSN
  • 0022-1082 (ISSN-L)
  • 1540-6261
OpenAlex
W2890190386
Cited by
675
References
23
FWCI
37.68
Type
journal article
Language
en
Open access
green

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This page
CoChat. (2026). Citation check: Decoding Inside Information. https://app.cochat.ai/papers/10.1111/j.1540-6261.2012.01740.x
The paper
COHEN, L., MALLOY, C., & POMORSKI, L. (2012). Decoding Inside Information. The Journal of Finance. https://doi.org/10.1111/j.1540-6261.2012.01740.x

Data sources

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.