Can you make profits by trading on corporate disclosures using machine learning? In this study, we aim to obtain a conservative estimate of profitability, while accounting for the combination of several important real-world aspects. Specifically, we consider the holistic research problem that combines model predictions based on the textual content of corporate disclosures and trading strategies while accounting for transaction costs, order clearance periods, post-publication returns, and liquidity filtering. Furthermore, we aim to understand how the resulting profits are influenced by different model and trading strategy parameters. Based on 354,992 form 8-K filings and 10,204 ad hoc announcements, we find that the proposed trading strategies yield up to 7.81 % and 9.34 % out-of-sample annualized return. In addition, our results suggest that machine learning models should be provided with additional features about prior disclosures, while being trained on the ternary prediction problem that allows for predictions of neutral market reactions. We complement our results with several sensitivity analyses that show how profitability is influenced by transaction costs, different ensemble sizes, return neutrality thresholds, and liquidity filtering. Ultimately, we provide useful insights for practitioners by describing how the machine learning models arrive at decisions in terms of Shapley Additive Explanations.

您可以通过使用机器学习对公司信息披露进行交易来获利吗？在这项研究中，我们的目标是获得对盈利能力的保守估计，同时考虑到几个重要的现实世界方面的组合。具体来说，我们考虑将基于公司披露文本内容和交易策略的模型预测结合起来，同时考虑交易成本、订单清算期、发布后回报和流动性过滤的整体研究问题。此外，我们旨在了解不同模型和交易策略参数如何影响最终利润。基于 354,992 份 8-K 表格备案和 10,204 份临时公告，我们发现拟议的交易策略收益率分别高达 7.81  % 和 9.34 样本外年化回报率百分比。此外，我们的结果表明，机器学习模型应该提供有关先前披露的额外特征，同时接受三元预测问题的训练，以预测中性市场反应。我们用几个敏感性分析来补充我们的结果，这些分析表明交易成本、不同的整体规模、回报中性阈值和流动性过滤如何影响盈利能力。最终，我们通过描述机器学习模型如何根据 Shapley 加法解释做出决策，为从业者提供有用的见解。