Informed Trading Intensity
Speaker
Time
Friday, June 6, 2025 3:00 PM - 4:00 PM
Venue
A3-2a-302
Online
Zoom 637 734 0280
(BIMSA)
Abstract
Informed trading is a critical topic in financial research because it directly relates to core issues such as market efficiency, price discovery, liquidity costs, and regulatory policy. In this lecture, I will present a paper titled "Informed Trading Intensity" from The Journal of Finance. This study employs a machine learning algorithm (XGBoost) to develop a novel measure of informed trading—Informed Trading Intensity (ITI)—using a specific class of informed trading data. The effectiveness of this measure lies in its ability to capture nonlinearities and interactions between informed trading, volume, and volatility through the machine learning approach. This data-driven methodology sheds light on the economics of informed trading, including impatient informed trading, commonality in informed trading, and models of informed trading.
Speaker Intro
Qiqi Gu is a PhD student at BIMSA and UCAS. Her research interests include digital economy, asset pricing and financial mathematics.