Trading Against the Random Expiration of Private Information: A Natural Experiment

Abstract

For years, the Securities and Exchange Commission (SEC) accidentally distributed securities disclosures to some investors before the public. We exploit this setting, which is unique because the delay until public disclosure was exogenous and the private information window was well defined, to study informed trading with a random stopping time. Trading intensity and the pace at which prices incorporate information decrease with the expected delay until public release, but the relation between trading intensity and time elapsed varies with traders' learning process. Noise trading and relative information advantage play similar roles as in standard microstructure theories assuming a fixed time window.

Price revelation from insider trading: Evidence from hacked earnings news

“…Taking this behavior as given, informed traders strategically split or time their trades when possible to avoid revealing themselves to market makers. Existing research that "tests" models of informed trading focuses its empirical tests on how informed traders structure their trades when trying to profit from their information (e.g., Koudijs, 2015, Kacperczyk and Pagnotta, 2021, Shkilko, 2018, Bolandnazar, Jackson Jr, Jiang, and Mitts, 2020. However, to our knowledge, existing research has not examined the ability of market makers to respond to increased informed trading volume, a critical assumption for these models.…”

“…3 They find that nearly 80% of insiders split their trades as in Kyle (1985) and are more likely to split when private signals are less powerful and legal risks are higher. 4 Bolandnazar, Jackson Jr, Jiang, and Mitts (2020) study the trade patterns when a subset of investors received value-relevant 2 Most of the traders involved in this illegal trading scheme were retail traders who were members of the hacker's extended families or their social circles. They frequently traded using their personal brokerage accounts.…”

How to Talk When a Machine is Listening: Corporate Disclosure in the Age of AI

“…That is, the first directional trade is the first buy (sell) trade at a price below (above) the terminal value, where buy-and sell-initiated trades are classified by theLee and Ready (1991) algorithm. As inBolandnazar et al (2020), we focus on the 15-minute window in order to isolate the effect of the filing; and hence both variables are censored at the end of the time window.The results, reported in Table4, support the prediction that high Machine Downloads are associated with faster trades after a filing becomes publicly available. A one-standard deviation increase in Machine Downloads saves 8.56 to 14.73 seconds for the first trade and 13.29 to 21.80…”

“…)There are two versions for the dependent variable: Time to the First Trade and Time to the First Directional Trade, the construction of which followBolandnazar, Jackson, Jiang, and Mitts (2020). seconds for the first directional trade.…”

“…There was a multi-year episode of early leakage, which was largely resolved inmid-2015. See Bolandnazar, Jackson, Jiang, andMitts (2020).12 See, e.g., "Cohen's Point72 Hires 30 People for Big Data Investing," Simone Foxman, Bloomberg, March 10, 2015, and "BlackRock Uses Big Data for Big Gains," Sarah Max,Barron's, December 26, 2015. …”