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Cited by 291

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

The Relation Between Insider‐Trading Restrictions and Executive Compensation

Open access
Roulstone, Darren T.·2003·Journal of Accounting Research

Abstract

In this study I investigate the relation between firm‐level insider‐trading restrictions and executive compensation. Using a trading‐window proxy for the existence of such restrictions, I test predictions that insiders will demand compensation for these restrictions and that firms will need to increase incentives to restricted insiders. I find that firms that restrict insider trading pay a premium in total compensation relative to firms not restricting insider trading, after controlling for economic determinants of pay. Furthermore, these firms use more incentive‐based compensation and their insiders hold larger equity incentives relative to firms that do not restrict insider trading. These results hold after controlling for the endogenous decision to restrict insiders and are consistent with the notion that insider trading plays a role in rewarding and motivating executives.

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Metadata

Authors
Roulstone, D. T.
Venue
Journal of Accounting Research
Publisher
Wiley
Published
2003-04-17
Volume
41
Issue
3
Pages
525-551
ISSN
  • 0021-8456 (ISSN-L)
  • 1475-679X
OpenAlex
W2026122661
Cited by
291
References
2
FWCI
14.92
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: The Relation Between Insider‐Trading Restrictions and Executive Compensation. https://app.cochat.ai/papers/10.1111/1475-679x.00115
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Roulstone, D. T. (2003). The Relation Between Insider‐Trading Restrictions and Executive Compensation. Journal of Accounting Research. https://doi.org/10.1111/1475-679x.00115

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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.