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Informed Trading Intensity

72 Pages Posted: 15 Jun 2021 Last revised: 6 Feb 2022

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; Canadian Derivatives Institute

Date Written: January 28, 2022

Abstract

We train a machine learning method on three classes of informed trades---activist trades, insider trades, and short sales---to develop a new measure of informed trading, the Informed Trading Intensity ("ITI"). ITI trained on one class of informed trades detects other classes of informed trades, pointing to commonalities in how informed investors trade. ITI measures increase before earnings, M\&A, and news announcements, and have implications for return reversal and asset pricing. ITI is effective because it captures nonlinearities and interactions between informed trading, volume, and volatility. Overall, learning from informed trading data can generate an effective informed trading measure.

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 (January 28, 2022). 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

Canadian Derivatives Institute ( email )

3000, chemin de la Côte-Sainte-Catherine
Montréal, Québec H3T 2A7
Canada

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