This document summarizes research on using structured event representations extracted from news articles to predict stock price movements. Key points include:
- Events are extracted from articles and represented as tuples of actors, actions, and objects to capture the who, what, when of events.
- A deep neural network model is used to predict stock price changes based on extracted event representations.
- The model achieves better performance than baselines that use bag-of-words representations of articles.