Market Microstructure: Trading on Information
Paper Session
Sunday, Jan. 9, 2022 12:15 PM - 2:15 PM (EST)
- Chair: Ingrid Werner, Ohio State University
How Much Insider Trading Happens in Stock Markets?
Abstract
We estimate that the actual prevalence of illegal insider trading is at least four times greater than the number of prosecutions, therefore, what we see in prosecutions is the tip of the iceberg. Using novel structural estimation methods (i.e., detection controlled estimation) that explicitly account for the incomplete and non-random detection and hand-collected data of all US prosecuted insider trading cases, we estimate that insider trading occurs in one in five mergers and acquisition events and in one in 20 earnings announcements. We further estimate that the probability of detection and prosecution of insider trading in both announcement types is approximately 15%. Our approach also identifies when and in which stocks is insider trading more likely to occur and what makes a given instance of insider trading more likely to be detected and prosecuted. Key drivers of the decision to engage in illegal insider trading include stock liquidity, the value of the inside information, and the number of people in possession of the information. Detection and prosecution are more likely when there are abnormal trading patterns and more regulatory resourcing. Our findings can be used by regulators to focus their enforcement efforts and make more efficient use of regulatory resources, this in turn can have a deterrence effect on an individual’s decision to illegally trade.When Do Informed Short Sellers Trade? Evidence from Intraday Data and Implications for Informed Trading Models
Abstract
Using more than five years of recent intraday short sale data from FINRA, we examine the time patterns and information content of off-exchange short sales throughout the trading day. We find that short sales near the open (before 10am) are informative about the cross-section of returns only at short horizons (through the following day), while off-exchange short sales during the middle of the trading day, particularly short sales involving an institution as identified via a novel algorithm, are informed at longer horizons (up to six weeks). We interpret our results using different dynamic models of informed trading, including Kyle (1985) and Holden and Subrahmanyam (1992), among others. If their private information is obtained overnight and is likely to quickly become stale or public, short sellers tend to take their positions fairly rapidly near the open. If their information is likely to remain private for longer, short sellers trade more gradually throughout the trading day, and this private information is incorporated into price in the following days or weeks, consistent with the dynamic version of Kyle (1985). Public release of negative fundamental information about earnings is considerably more likely shortly after heavy shorting near the open.The Conduits of Price Discovery: A Machine Learning Approach
Abstract
When examining information flow into prices, empirical studies tend to focus on order submissions. Meanwhile, theory suggests that market conditions should play important roles in augmenting and regulating information flows. Using a machine learning technique known as reinforcement learning, we show that price discovery is notably affected by such theory-based conditions as the state of the limit order book, price history, bid-ask spread, and order arrival frequency. The state of the book and price history stand out as conditions whose importance rivals that of order submissions. The conditions have a substantial influence on trading by sophisticated market participants.Discussant(s)
Laura Veldkamp
,
Columbia University
Emiliano Pagnotta
,
Imperial College London
Matthew Ringgenberg
,
University of Utah
Barbara Rindi
,
Bocconi University
JEL Classifications
- G1 - Asset Markets and Pricing