Insider trading and the algorithmic trading environment

• Published date: 01 December 2022
• Author: Millicent Chang,John Gould,Yuyun Huang,Sirimon Treepongkaruna,Joey Wenling Yang
• Date: 01 December 2022
• DOI: http://doi.org/10.1111/irfi.12367

ORIGINAL ARTICLE

Insider trading and the algorithmic trading

environment

Millicent Chang

1

| John Gould

2

| Yuyun Huang

3

|

Sirimon Treepongkaruna

3

| Joey Wenling Yang

3

1

UOW School of Business, University of

Wollongong, Wollongong, Australia

2

Curtin Business School, Curtin University,

Perth, Australia

3

UWA Business School, The University of

Western Australia, Perth, Australia

Correspondence

Millicent Chang, UOW School of Business,

University of Wollongong, Wollongong,

Australia.

Email: mchang@uow.edu.au

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 counter-

act this effect by releasing information to the market. Our

findings reinforce the important role of insider trading in

providing fundamental information and aiding price discov-

ery, especially in an era of computerized financial markets.

1|INTRODUCTION

Securities trading today is highly automated with computer algorithms automatically executing specified trading

strategies (Hendershott et al., 2011). Such algorithmic trading (AT) has become such a significant part of market

structure that high frequency trading (HFT) accounts for 60% of daily US equity trading volume (Meyer et al., 2018).

While robust debate continues on AT's help or hindrance effect on market quality (Menkveld, 2016), our focus in this

paper is on price discovery. Generally, traders promote price discovery by acquiring new information and by incorpo-

rating existing information into prices. A trade-off exists such that traders who incorporate existing information into

Received: 6 March 2021 Revised: 20 June 2021 Accepted: 28 September 2021

DOI: 10.1111/irfi.12367

© 2021 International Review of Finance Ltd.

International Review of Finance. 2022;22:725–750. wileyonlinelibrary.com/journal/irfi 725

prices (improving price efficiency) may themselves dissuade information acquisition, resulting in decreased price effi-

ciency (Fama, 1970). Similar arguments are made for regulating insider trading in Manne (1966) and Fishman and

Hagerty (1992). With AT, Zhang (2017) and Chakrabarty et al. (2019) show that algorithmic traders incorporate pub-

lic information into prices swiftly while Weller (2018) reports reduced price informativeness due to a decrease in

information acquisition. Bilinski et al. (2020) show similar reduction in information acquisition among analysts. In this

paper, given AT induced changes specifically to price efficiency and information acquisition, we examine how the

trading environment has changed for corporate insiders in terms of their incentives to trade and the resulting returns

from their trades.

The trading activity of insiders has been shown to predict future returns (Huddart & Ke, 2007; Jaffe, 1974;Ke

et al., 2003; Lin & Howe, 1990; Seyhun, 1986), based on the hypothesis that these trades are made by insiders

possessing superior information about firm future prospects. These informative trades also produce abnormal

returns. Insiders are also known to trade for non-information or non-profit maximizing reasons to diversify their

holdings or for personal liquidity (Huddart & Ke, 2007; Ke et al., 2003). Kallunki et al. (2018) have added rebalancing

objectives, tax reasons, and behavioral biases as incentives for insider trading. Consequently, the market reacts

asymmetrically to purchases and sales because fundamentally they convey different information. Purchases are asso-

ciated with larger market reactions (Fidrmuc et al., 2006; Lakonishok & Lee, 2001) because insiders are committing

their own funds and holding less optimally diversified portfolios when conveying positive news about firm future

prospects while sales convey negative information with potential exposure to litigation risk and adverse publicity.

Perhaps the closest group of traders to corporate insiders are institutional traders where Van Kervel and Men-

kveld (2019) find that these traders use their information strategically in the AT and HFT environment, swapping

higher returns for lower risk of being detected. Garriott and Riordan (2020) report that informed traders are strate-

gic, timing their trades to minimize price impact. Therefore, how AT changes corporate insiders' motivation to trade

and the returns from their trades depends on whether the trades are information motivated. We expect insiders to

trade in a similar way to institutional traders who time their trades strategically for minimal price impact.

To understand how AT affects the trading behavior of insiders, we have to appreciate how it affects the infor-

mation environment and in turn, how the changed information environment affects insiders' incentive to trade.

According to O'Hara (2003), financial markets provide liquidity and enable price discovery such that information is

incorporated into prices. Studies have shown that AT aids markets on both these aspects by faster incorporation of

public information into prices (Brogaard et al., 2014; Chakrabarty et al., 2019; Zhang, 2017) and increasing liquidity

via improvements in quote efficiency (Hendershott et al., 2011). Therefore, price informativeness increases with AT

activity. However, Weller (2018) argues that price discovery occurs by the acquisition of new information and by the

incorporation of existing information into prices. This important distinction between existing and acquirable informa-

tion was demonstrated in Lee and Watts (2021) and Weller (2018) where AT leads to less information acquisition

and lower price efficiency.

From this discussion,, we argue that, being informed, insiders are similarly strategic in their choice of type of

trade (purchase or sale), timing of trade (routine or non-routine) and size of trade (large or small). The economic con-

sequence of their trading strategy in an AT environment will thus depend on whether the trade is motivated by infor-

mation. If the trade is information motivated, insiders are considered and deliberate, and more likely to trade

strategically to conceal their information advantage (Biggerstaff et al., 2020). As a result, insider profits will not be

affected by improved information environment due to AT. This view is supported by Garriott and Riordan's (2020)

finding that informed traders' profits are unchanged in HFT times. On the other hand, Korajczyk and Murphy (2019)

indicate that HFT is associated with lower transaction costs for small, uninformed trades and higher costs for large

informed ones. Therefore, how AT affects insider trading returns remains an empirical question.

Using SEC Form 4 filings of insider transactions over the period 2000 to 2010, we show that while AT has no

effect on the returns from insider purchases, it reduces returns for routine insider sales, supporting our hypothesis

that AT attenuates insider profits for non-information driven trades only. We further show that such evidence is

stronger in our sample where the trade size is larger, the trade contains less information content, and the stock of a

726 CHANG ET AL.