When machines trade on corporate disclosures: Using text analytics for investment strategies

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.

Automated summary

Can machine learning help in making profits through trading on corporate disclosures? This study explores this question and provides insights into the profitability of trading strategies based on textual content of corporate disclosures. The results show significant annualized returns and suggest the importance of additional features and neutral market predictions in machine learning models.