This project uses the Post Earnings Announcement Drift (PEAD) anomaly to generate trade recommendations by analyzing earnings surprises. A web-based interface displays these recommendations and 10 years of backtesting results. Real-time data ingestion and machine learning models assist in making trading decisions after every earnings release.
Instructor: Mehdi Moradi
TA: Amirhossein Sabour