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Cited by 543

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Insider Trading and Voluntary Disclosures

Open access
CHENG, QIANG, LO, KIN·2006·Journal of Accounting Research

Abstract

We hypothesize that insiders strategically choose disclosure policies and the timing of their equity trades to maximize trading profits, subject to the litigation costs associated with disclosure and insider trading. Accounting for endogeneity between disclosures and trading, we find that when managers plan to purchase shares, they increase the number of bad news forecasts to reduce the purchase price. In addition, this relation is stronger for trades initiated by chief executive officers than for those initiated by other executives. Confirming this strategic behavior, we find that managers successfully time their trades around bad news forecasts, buying fewer shares beforehand and more afterwards. We do not find that managers adjust their forecasting activity when they are selling shares, consistent with higher litigation concerns associated with insider sales. Overall, our evidence suggests that insiders do exploit voluntary disclosure opportunities for personal gain, but only selectively, when litigation risk is sufficiently low.

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Crossref · 10.1111/j.1475-679x.2006.00222.x

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Metadata

Authors
CHENG, Q., & LO, K.
Venue
Journal of Accounting Research
Publisher
Wiley
Published
2006-10-24
Volume
44
Issue
5
Pages
815-848
ISSN
  • 0021-8456 (ISSN-L)
  • 1475-679X
OpenAlex
W3125695164
Cited by
543
References
1
FWCI
30.86
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Insider Trading and Voluntary Disclosures. https://app.cochat.ai/papers/10.1111/j.1475-679x.2006.00222.x
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CHENG, Q., & LO, K. (2006). Insider Trading and Voluntary Disclosures. Journal of Accounting Research. https://doi.org/10.1111/j.1475-679x.2006.00222.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.