Trade the Event: Corporate Events Detection for News-Based Event-Driven Trading

Abstract

In this paper, we introduce an event-driven trading strategy that predicts stock movements by detecting corporate events from news articles. Unlike existing models that utilize textual features (e.g., bag-of-words) and sentiments to directly make stock predictions, we consider corporate events as the driving force behind stock movements and aim to profit from the temporary stock mispricing that may occur when corporate events take place. The core of the proposed strategy is a bi-level event detection model. The low-level event detector identifies events' existences from each token, while the high-level event detector incorporates the entire article's representation and the low-level detected results to discover events at the article-level. We also develop an elaborately-annotated dataset EDT for corporate event detection and news-based stock prediction benchmark. EDT includes 9721 news articles with token-level event labels as well as 303893 news articles with minute-level timestamps and comprehensive stock price labels. Experiments on EDT indicate that the proposed strategy outperforms all the baselines in winning rate, excess returns over the market, and the average return on each transaction.

arXiv commentAccepted to publish in Findings of ACL 2021

References in corpus (4)

• Predicting the Effects of News Sentiments on the Stock Market
• Stock trend prediction using news sentiment analysis
• Stock Market Forecasting Based on Text Mining Technology: A Support Vector Machine Method
• Leveraging Financial News for Stock Trend Prediction with Attention-Based Recurrent Neural Network