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

Wei Jiang is the Arthur F. Burns Professor of Free and Competitive Enterprise at Columbia Business School; Robert J. Jackson, Jr. is Professor of Law at New York University School of Law; Mohammadreza Bolandnazar is a PhD Candidate in Finance at Columbia Business School; and Joshua Mitts is Associate Professor of Law at Columbia Law School. This post is based on their recent paper, forthcoming in the Journal of Finance.

For years, unbeknownst to lawmakers and the public, a small group of private investors were inadvertently given access to securities filings before they were widely released via EDGAR. A government contractor operating a platform known as the Public Dissemination Service, or PDS, distributed SEC filings to a small number of paying subscribers moments before they reached the public. In October 2014, the Wall Street Journal exposed this issue, and the issue was largely fixed under regulatory pressure.

There is no evidence that either the SEC or the government contractor acted opportunistically. Nevertheless, the episode provides a rare lab-like setting for studying how speculators trade on, and the stock market processes, private information that expires at a random time. More specifically, the incidence provides two unique features that allow us to study informed trading with a random stopping time. First, we can observe both the arrival time and the content of private information, filling the gap in direct empirical testing of trading based on private information due to the challenge that private information, by definition, is not public knowledge and thus neither the timing of its arrival nor its content are generally observable by econometricians. Second, we can accurately measure the length of time that the informed traders have the information before a filing reaches the public. Critically, the delay was beyond the control of both the filers and the speculators, and is not correlated with any economic variables of interest. Moreover, because the informed traders could form expectations about the length of the delay based on factors (notably internet traffic during given time of the day) that were exogenous to traders’ behavior, we are able to identify the causal impact of the expected delay on trading patterns as well as show how market prices incorporate private information that is based on the expected length of the delay.

Our entire data set includes all securities disclosures that public companies electronically filed with the SEC from June 25 to October 15, 2014. Matching to the standard databases for publicly traded companies and imposing additional filters that rule out excessive delays yield our final sample of 36,352 filings. Of this sample, 18% are Form 8 filings (involving disclosure of material corporate events), 51% Form 4 filings (disclosure of insiders’ trades in the company’s stock; current U.S. securities law requires that insiders provide such disclosure within 48 hours of each trade), 1% Schedule 13D filings (disclosure of beneficial ownership of 5% or more; current law requires that this Schedule be filed within 10 days after the investor crosses the 5% threshold), and 30% other miscellaneous types (such as 10-Q filings). A great majority (over 95%) of the filings in this sample are non-scheduled, that is, are contingent on unanticipated events rather than a predetermined filing date. The average delay is 78 minutes, with an interquartile range of 7 to 108 minutes.

To provide a unified framework for our empirical tests, we first develop a theory model of informed trading with a random stopping time but a known hazard rate to public disclosure. We test the model on the merged data of the filings with early leakage and trade-level information of the Trade and Quote (TAQ) database. We find that when the expected delay, proxied by the average delay in the hour of the day—mainly affected by the internet traffic volume, is longer, insiders trade more patiently, in terms of both the dollar value of trades and the number of trades. In addition, when the expected delay is longer, insiders take more time to submit the first trade after the leakage. Moreover, both the information content and the ease of information processing of the different types of filings affect trading speed and intensity. Schedule 13D filings, which tend to generate more sizable abnormal returns upon public announcement (as compared to, for example, Form 4 filings) and are relatively straightforward to process (as compared to, for example, Form 8-K filings), induce the fastest and most intense trading during the private window.

Finally, we take advantage of this unique setting to test the standard predictions of an informed trading model. We find that speculators trade more aggressively when the value-price divergence is larger, when the filing entails high information content (measured ex ante or ex post), and when the market is deeper (measured ex ante or in real time). We also find that trading via limit orders is preferred to trading via market orders when there is more time (that is, when the expected delay is longer) and when market-wide trading volume is high and volatility is low. Although insiders attempt to smooth out the price impact of their trading, we find that information disseminates faster among large-cap, high-turnover stocks and when the overall market experiences more trading activity.

The quasi-natural experiment allows us to test informed trading strategies against a random deadline and the process through which private information is impounded into stock prices. Our results are consistent with the hypotheses that trading intensity and the pace at which prices incorporate information decrease with the expected delay until public release, while noise trading and relative information advantage play similar roles as in standard microstructure theories assuming a fixed time window.

The complete paper is available here.

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