Informed Trading Intensity

Abstract

We train a machine-learning method on a class of informed trades to develop a new measure of informed trading, the Informed Trading Intensity (ITI). We show that the method works well because it captures nonlinearities and interactions in the relation between informed trading, volume, and volatility. Though ITI is trained on a particular class of informed trades, trades executed by activist investors, the measure also increases on days with opportunistic insider trades and on days with large changes in short interest, indicating that ITI captures the commonality in how different classes of informed investors trade. Moreover, it detects various informational events, including stock price reactions to earnings surprises, M&A announcements, and unscheduled news releases. Returns on days with high ITI reverse less than returns on other days. In the cross-section, higher ITI is associated with higher returns next month. Our main insight is that learning from data on informed trades can generate an effective measure of informed trading.