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

机译:使用遗传算法优化XGBoost捕获盈利后公告漂移的动态

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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.
机译:收益后公告漂移(PAED)是股市现象现象,当股票的累计异常回报倾向于在盈利公告之后在近期收益的盈利方向令人惊讶的方向。虽然它是最受研究的股票市场异常之一,但目前的文献通常是有限的,这些文献通常是通过使用更简单的回归方法的少数因素来解释这种现象。在本文中,我们使用基于机器学习的方法,并旨在使用来自一大群库存的数据和各种基本和技术因素来捕获Pead Dynamics。我们的模型围绕着极端梯度提升(XGBoost)构建,并根据1997年至2018年间罗素1000指数的1,106家公司的季度财务公告数据使用长期的工程输入特征列表。我们对PEACE预测和分析进行了众多实验对文献有以下贡献。首先,我们展示了如何使用机器学习方法分析收益后发布漂移,并证明这种方法在可信地预测漂移方向上。它是第一次使用XGBoost研究了Pead Dynamics。我们表明漂移方向是由不同工业部门的股票的不同因素驱动,并且在不同的季度和XGBoost方面都有效地了解变化的动态。其次,我们表明,通过遗传算法优化的XGBoost良好优化,可以帮助分配出样的股票,以形成更高的积极返回的投资组合,而且投资组合具有较低的负返回,这是可以在过程中采用的发现发展市场中立策略。第三,我们展示了理论事件驱动的股票策略如何努力与现实不断变化的市场价格努力,降低了它们的效力。我们展示了一段策略来纠正在船舶信号交易时购买进入移动市场的难度。

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