Price Revelation from Insider Trading: Evidence from Hacked Earnings News

66 Pages Posted: 16 Apr 2019 Last revised: 29 Nov 2021

See all articles by Pat Akey

Pat Akey

University of Toronto - Rotman School of Management

Vincent Gregoire

HEC Montreal - Department of Finance

Charles Martineau

University of Toronto - Rotman School of Management and UTSC Management

Date Written: October 20, 2021

Abstract

From 2010 to 2015, a group of traders illegally accessed earnings information before their public release by hacking several newswire services. We use this scheme as a natural experiment to investigate how informed investors select among private signals and how efficiently financial markets incorporate private information contained in trades into prices. We construct a measure of qualitative information using machine learning and find that the hackers traded on both qualitative and quantitative signals. The hackers’ trading caused 15% more of the earnings news to be incorporated in prices before their public release. Liquidity providers responded to the hackers’ trades by widening spreads.

Keywords: cyber risks, earnings announcements, insider trading, market price efficiency, machine learning

JEL Classification: G10, G12, G14

Suggested Citation

Akey, Pat and Gregoire, Vincent and Martineau, Charles, Price Revelation from Insider Trading: Evidence from Hacked Earnings News (October 20, 2021). Journal of Financial Economics (JFE), Forthcoming, Available at SSRN: https://ssrn.com/abstract=3365024 or http://dx.doi.org/10.2139/ssrn.3365024

Pat Akey (Contact Author)

University of Toronto - Rotman School of Management ( email )

105 St. George Street
Toronto, Ontario M5S 3E6 M5S1S4
Canada

Vincent Gregoire

HEC Montreal - Department of Finance ( email )

3000 Chemin de la Cote-Sainte-Catherine
Montreal, Quebec H3T 2A7
Canada

Charles Martineau

University of Toronto - Rotman School of Management and UTSC Management ( email )

105 St-George
Toronto, Ontario M5S3E6
Canada

HOME PAGE: http://charlesmartineau.com

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