  Considering event structure information has proven helpful in text-based
stock movement prediction. However, existing works mainly adopt the
coarse-grained events, which loses the specific semantic information of diverse
event types. In this work, we propose to incorporate the fine-grained events in
stock movement prediction. Firstly, we propose a professional finance event
dictionary built by domain experts and use it to extract fine-grained events
automatically from finance news. Then we design a neural model to combine
finance news with fine-grained event structure and stock trade data to predict
the stock movement. Besides, in order to improve the generalizability of the
proposed method, we design an advanced model that uses the extracted
fine-grained events as the distant supervised label to train a multi-task
framework of event extraction and stock prediction. The experimental results
show that our method outperforms all the baselines and has good
generalizability.
