Capturing dynamics of post-earnings-announcement drift using a genetic algorithm-optimized XGBoost

• Published in: Expert systems with applications 2021-09, Vol.177, p.114892, Article 114892
• Main Authors: Ye, Zhengxin Joseph, Schuller, Björn W.
• Format: Article
• Language: English
• Subjects: Anomalies; Drift; Dynamics; Financial trading; Genetic algorithm; Genetic algorithms; Machine learning; Post-earnings-announcement drift; Pricing; Profits; Securities markets; Stock exchanges; XGBoost
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2021.114892

•Predict cumulative abnormal return of stocks following earnings release using XGBoost.•Study drivers of post-earnings-release drift (PEAD) as identified by XGBoost.•Remedy the difficulty of buying into a moving market when trading on PEAD signals. Post-Earnings-Announcement Drift (PEAD) is a stock market phenomenon when a stock’s cumulative abnormal return has a tendency to drift in the direction of an earnings surprise in the near term following an earnings announcement. Although it 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 credibly forecasting the drift direction. It is the first time PEAD dynamics are studied using XGBoost. We show that the drift direction is driven by different factors for stocks from different industrial sectors and in different quarters and XGBoost is effective in understanding the changing dynamics. 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 trading on PEAD signals.