Insider trading and the algorithmic trading environment

Millicent Chang, John Gould, Yuyun Huang, Sirimon Treepongkaruna and Joey Wenling Yang

International Review of Finance, 2022, vol. 22, issue 4, 725-750

Abstract: We 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.

Date: 2022
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