使用遗传算法优化的监督学习捕获盈利公告后漂移的动态
Capturing dynamics of post-earnings-announcement drift using genetic algorithm-optimised supervised learning
摘要：
虽然盈余公告后漂移（PEAD）是研究最多的股票市场的异常之一，目前的文献往往是有限的解释这一现象的少数因素，使用简单的回归方法。在本文中，我们使用基于机器学习的方法，旨在使用来自大量股票的数据以及广泛的基本面和技术因素来捕获PEAD动态。我们的模型是围绕极端梯度提升（XGBoost）构建的，并使用了一长串基于1997年至2018年罗素1000指数中1,106家公司季度财务公告数据的工程输入特征。我们对PEAD的预测和分析进行了大量的实验，并对文献做出了以下贡献。首先，我们展示了如何使用机器学习方法分析收益公告后漂移，并展示了这种方法在漂移方向上产生可信预测的能力。这是首次使用XGBoost研究PEAD动力学。我们表明，不同行业和不同季度的股票的漂移方向实际上是由不同因素驱动的，XGBoost可以有效地理解不断变化的驱动因素。第二，我们表明，一个XGBoost以及优化的遗传算法可以帮助分配样本外的股票，形成投资组合具有较高的正回报长期和投资组合具有较低的负回报短期，一个发现，可以通过在发展过程中的市场中性策略。第三，我们展示了理论上的事件驱动股票策略如何在现实中应对不断变化的市场价格，从而降低了它们的有效性。我们提出了一种策略，以弥补在处理PEAD信号时买入移动市场的困难。
英文摘要：
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.
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