Informed Trading Intensity

Journal of Finance, Forthcoming

93 Pages Posted: 15 Jun 2021 Last revised: 11 Jul 2023

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: June 27, 2023

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''). ITI increases before earnings, M&A, and news announcements, and has implications for return reversal and asset pricing. ITI is effective because it captures nonlinearities and interactions between informed trading, volume, and volatility. This data-driven approach can shed light on the economics of informed trading, including impatient informed trading, commonality in informed trading, and models of informed trading. 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 (June 27, 2023). Journal of Finance, Forthcoming, 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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