  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. Our model is built around the
Extreme Gradient Boosting (XGBoost) and uses a long list of engineered input
features based on quarterly financial announcement data from 1,106 companies in
the Russell 1000 index between 1997 and 2018. We perform numerous experiments
on PEAD predictions and analysis and have the following contributions to the
literature. First, we show how Post-Earnings-Announcement Drift can be analysed
using machine learning methods and demonstrate such methods' prowess in
producing credible forecasting on the drift direction. It is the first time
PEAD dynamics are studied using XGBoost. We show that the drift direction is in
fact driven by different factors for stocks from different industrial sectors
and in different quarters and XGBoost is effective in understanding the
changing drivers. Second, we show that an XGBoost well optimised by a Genetic
Algorithm can help allocate out-of-sample stocks to form portfolios with higher
positive returns to long and portfolios with lower negative returns to short, a
finding that could be adopted in the process of developing market neutral
strategies. Third, we show how theoretical event-driven stock strategies have
to grapple with ever changing market prices in reality, reducing their
effectiveness. We present a tactic to remedy the difficulty of buying into a
moving market when dealing with PEAD signals.

Statistical Finance;Machine Learning