Beijing Institute of Mathematical Sciences and Applications Beijing Institute of Mathematical Sciences and Applications

  • About
    • President
    • Governance
    • Partner Institutions
    • Visit
  • People
    • Management
    • Faculty
    • Postdocs
    • Visiting Scholars
    • Administration
    • Academic Support
  • Research
    • Research Groups
    • Courses
    • Seminars
  • Join Us
    • Faculty
    • Postdocs
    • Students
  • Events
    • Conferences
    • Workshops
    • Forum
  • Life @ BIMSA
    • Accommodation
    • Transportation
    • Facilities
    • Tour
  • News
    • News
    • Announcement
    • Downloads
About
President
Governance
Partner Institutions
Visit
People
Management
Faculty
Postdocs
Visiting Scholars
Administration
Academic Support
Research
Research Groups
Courses
Seminars
Join Us
Faculty
Postdocs
Students
Events
Conferences
Workshops
Forum
Life @ BIMSA
Accommodation
Transportation
Facilities
Tour
News
News
Announcement
Downloads
Qiuzhen College, Tsinghua University
Yau Mathematical Sciences Center, Tsinghua University (YMSC)
Tsinghua Sanya International  Mathematics Forum (TSIMF)
Shanghai Institute for Mathematics and  Interdisciplinary Sciences (SIMIS)
BIMSA > BIMSA Digital Economy Lab Seminar Informed Trading Intensity
Informed Trading Intensity
Organizers
Ruize Gao , Liyan Han , Zhen Li , Fei Long , Ke Tang
Speaker
Qiqi Gu
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.
Beijing Institute of Mathematical Sciences and Applications
CONTACT

No. 544, Hefangkou Village Huaibei Town, Huairou District Beijing 101408

北京市怀柔区 河防口村544号
北京雁栖湖应用数学研究院 101408

Tel. 010-60661855 Tel. 010-60661855
Email. administration@bimsa.cn

Copyright © Beijing Institute of Mathematical Sciences and Applications

京ICP备2022029550号-1

京公网安备11011602001060 京公网安备11011602001060