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While Post-Earnings-Announcement Drift (PEAD) is one of the most studiedstock market anomalies, the current literature is often limited in explainingthis phenomenon by a small number of factors using simpler regression methods.In this paper, we use a machine learning based approach instead, and aim tocapture the PEAD dynamics using data from a large group of stocks and a widerange of both fundamental and technical factors. Our model is built around theExtreme Gradient Boosting (XGBoost) and uses a long list of engineered inputfeatures based on quarterly financial announcement data from 1,106 companies inthe Russell 1000 index between 1997 and 2018. We perform numerous experimentson PEAD predictions and analysis and have the following contributions to theliterature. First, we show how Post-Earnings-Announcement Drift can be analysedusing machine learning methods and demonstrate such methods' prowess inproducing credible forecasting on the drift direction. It is the first timePEAD dynamics are studied using XGBoost. We show that the drift direction is infact driven by different factors for stocks from different industrial sectorsand in different quarters and XGBoost is effective in understanding thechanging drivers. Second, we show that an XGBoost well optimised by a GeneticAlgorithm can help allocate out-of-sample stocks to form portfolios with higherpositive returns to long and portfolios with lower negative returns to short, afinding that could be adopted in the process of developing market neutralstrategies. Third, we show how theoretical event-driven stock strategies haveto grapple with ever changing market prices in reality, reducing theireffectiveness. We present a tactic to remedy the difficulty of buying into amoving market when dealing with PEAD signals.

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