Capturing dynamics of post-earnings-announcement drift using genetic algorithm-optimised supervised learning

7 Sep 2020Zhengxin Joseph YeBjorn W. Schuller

While Post-Earnings-Announcement Drift (PEAD) is one of the most studied stock market anomalies, the current literature is often limited in explaining this phenomenon by a small number of factors using simpler regression methods. In this paper, we use a machine learning based approach instead, and aim to capture the PEAD dynamics using data from a large group of stocks and a wide range of both fundamental and technical factors... (read more)

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