Does Algorithmic Trading Affect Analyst Research Production?

Bilinski, Pawel; Karamanou, Irene; Kopita, Anastasia; Panayides, Marios A.

doi:10.2139/ssrn.3597356

Cited by 2 publications

(5 citation statements)

References 111 publications

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“…With AT, Zhang (2017) and Chakrabarty et al (2019) show that algorithmic traders incorporate public 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.…”

Section: Introductionmentioning

confidence: 80%

“…The periods leading up to earnings announcements represent trading windows that will frequently be rich with valuable private information, hence these periods are worthy of special attention when considering the interaction between AT and insider trading 5 . Weller (2018), Bilinski et al (2020) and Garriott and Riordan (2020) indicate that AT has a detrimental effect on the informational efficiency of stock prices in the lead‐up to earnings announcements. From Weller (2018), we measure the Jump Ratio as the log of the absolute value of the ratio of the earnings announcement abnormal price change to the total pre‐announcement plus announcement abnormal price change:

italicJump_italicRatio goodbreak= \log (||, \frac{Δ p_{i, t}^{((), T goodbreak- 1, T goodbreak+ 2)}}{Δ p_{i, t}^{((), T goodbreak- 22, T goodbreak+ 2)}})

For earnings announcement event t for stock i , Δp it ( T –1, T +2) and Δp i , t ( T –22, T +2) are cumulated abnormal returns (from the Fama and French (1992) three factor model) over, respectively, the 3 day ( T – 1, T + 2) announcement window and the 24 day ( T − 22, T + 2) pre‐announcement plus announcement window 6 .…”

Section: Additional Analysismentioning

confidence: 99%

“…On the other hand, another stream of studies link AT to information acquisition. Weller (2018) report a reduction in the amount of information in prices of between 9% and 13% per standard deviation of AT activity in the month before earnings announcements and Bilinski et al (2020) indicates AT reducing analyst stock coverage and the number of research reports. While AT induces faster incorporation of information into price, especially around news release events, on the other hand, it reduces the amount of information in the market due to the disincentives to acquire or produce such information.…”

Section: Prior Literature and Expectationsmentioning

confidence: 99%

Section: Information Acquisitionmentioning

confidence: 99%

“…To our knowledge, our paper is the first to examine corporate insider trading in an AT environment, integrating studies on insider trading with the literature on the implications of AT on other market participants such as institutional traders and analysts. For example, Bilinski et al (2020) show a reduction in analyst research production due to AT obstructing trades on new information. We document a new role for insider trading, where information from these trades contribute to the information environment when there is a reduction in information acquisition due to AT.…”

Section: Introductionmentioning

confidence: 99%

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Insider trading and the algorithmic trading environment

et al. 2021

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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. | INTRODUCTIONSecurities 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 incorporating existing information into prices. A trade-off exists such that traders who incorporate existing information into

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