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用遗传算法调优XGBoost建模财报后漂移

文章 arXiv papers · 作者: Zhengxin Joseph Ye et al.
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总结

本文将监督式机器学习用于财报公告后漂移（PEAD），这是一种财务公告后收益继续变动的股票异常现象。模型使用XGBoost，以及基于1997至2018年间1,106家罗素1000指数公司季度公告数据构建的基本面和技术面特征。研究采用遗传算法优化模型，并分析漂移方向及其驱动因素如何随行业板块和日历季度变化。

作者报告称，模型能够可靠地预测漂移方向，并将样本外股票分为收益表现更好的多头和空头组；作者认为，这或可支持开发市场中性策略。论文还讨论了实施难点：事件驱动交易的进场过程中，价格可能发生变动，并提出一种旨在减少这一问题的做法。描述没有提供表现数据、交易成本估计或详细的验证程序，因此报告结果并不能证明净盈利能力或持续性。

核心观点

  • 研究使用XGBoost预测财报公告后漂移的方向。
  • 其构建的特征结合了季度公告前后的基本面和技术面信息。
  • 遗传算法用于优化模型，分析涵盖了1,106家1000指数公司，时间范围为1997至2018。
  • 据报告，漂移驱动因素会随行业板块和季度而变化。
  • 样本外股票排序和进场方法分别应对投资组合构建与价格变动问题，但文中未提供盈利能力细节。

标签

股票事件驱动机器学习回测投资组合构建
全文
# Capturing dynamics of post-earnings-announcement drift using genetic algorithm-optimised supervised learning


# Capturing dynamics of post-earnings-announcement drift using genetic algorithm-optimised supervised learning









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.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。