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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao International Review...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
International Review of Finance
Article . 2021 . Peer-reviewed
License: Wiley Online Library User Agreement
Data sources: Crossref
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Insider trading and the algorithmic trading environment

Authors: Millicent Chang; John Gould; Yuyun Huang; Sirimon Treepongkaruna; Joey Wenling Yang;

Insider trading and the algorithmic trading environment

Abstract

AbstractWe examine how algorithmic trading (AT) changes the trading environment for corporate insiders, specifically in terms of motivation to trade and timing of trade. Using SEC Form 4 insider filings and AT computed from the limit order book, we find that AT affects insiders' decisions to buy or sell, depending on whether the trades are information driven, resulting in changes in trading returns. AT reduces returns associated with routine insider sales by 0.9% of a change in AT. However being sophisticated and informed traders, insiders are able to trade strategically, leaving their purchase returns unaffected by AT. The results also show that while AT reduces information acquisition efforts in the pre‐earnings announcement period, insider trades counteract this effect by releasing information to the market. Our findings reinforce the important role of insider trading in providing fundamental information and aiding price discovery, especially in an era of computerized financial markets.

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    popularity
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    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
4
Top 10%
Average
Average
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