  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.
