Price Revelation from Insider Trading: Evidence from Hacked Earnings News
66 Pages Posted: 16 Apr 2019 Last revised: 29 Nov 2021
Date Written: October 20, 2021
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: Suggested Citation