Construct (python library)

Insider

Computer security

Literature

Economics

Computer science

Market liquidity

Business

Political science

Monetary economics

Earnings

Maturity (psychological)

Financial economics

Programming language

Hacker

Finance

Private information retrieval

Revelation

Insider trading

Journal of Financial Economics

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.

Price revelation from insider trading: Evidence from hacked earnings news