Informed Trading Intensity

55 Pages Posted: 15 Jun 2021

See all articles by Vincent Bogousslavsky

Vincent Bogousslavsky

Boston College - Department of Finance

Vyacheslav Fos

Boston College - Department of Finance; European Corporate Governance Institute (ECGI); Centre for Economic Policy Research (CEPR)

Dmitriy Muravyev

Michigan State University - Department of Finance

Date Written: June 12, 2021

Abstract

We train a state-of-the-art machine-learning method (ML) on a class of informed trades to develop a new measure of informed trading, the Informed Trading Intensity ("ITI"). Though the measure is trained on a particular class of informed trades, it predicts various informational events, including stock price reactions to earnings surprises, M&A announcements, and unscheduled news releases. The measure also increases on days with opportunistic insider trades and large changes in short interest. 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.

Keywords: Informed trading, machine learning, adverse selection, stock returns, intraday data

JEL Classification: G10, G12, G14

Suggested Citation

Bogousslavsky, Vincent and Fos, Vyacheslav and Muravyev, Dmitriy, Informed Trading Intensity (June 12, 2021). Available at SSRN: https://ssrn.com/abstract=3865990 or http://dx.doi.org/10.2139/ssrn.3865990

Vincent Bogousslavsky

Boston College - Department of Finance ( email )

Carroll School of Management
140 Commonwealth Avenue
Chestnut Hill, MA 02467-3808
United States

Vyacheslav Fos

Boston College - Department of Finance ( email )

Carroll School of Management
140 Commonwealth Avenue
Chestnut Hill, MA 02467-3808
United States

European Corporate Governance Institute (ECGI) ( email )

c/o the Royal Academies of Belgium
Rue Ducale 1 Hertogsstraat
1000 Brussels
Belgium

Centre for Economic Policy Research (CEPR) ( email )

London
United Kingdom

Dmitriy Muravyev (Contact Author)

Michigan State University - Department of Finance ( email )

315 Eppley Center
East Lansing, MI 48824-1122
United States

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