Deep Learning Post-Earnings-Announcement Drift

Post-Earnings-Announcement Drift (PEAD) has traditionally been studied using regression models in the literature which often involve smaller data sets and smaller groups of factors whose analysis results tend to be more linear in nature. In this paper, we explore using machine learning models to overcome those limitations and aim to find an optimal supervised model in forecasting drift direction following an earnings release. We test a deep neural network (DNN), an extreme gradient boosting model (XGB) as well as support vector machines (SVM) with different kernels and use a long list of carefully prepared and engineered input features including data from quarterly earnings reports from 1 106 companies in the Russell 1000 index between 1997 and 2018. We find that XGB performs marginally better than the considered DNN and both are significantly better than the SVM variants. We use both Cochran's Q Test and McNemar's Test to prove that our findings are statistically meaningful. We also find that movement of stocks in different industrial sectors respond differently to the same factors when using the same models and provided analysis on that.