  News events can greatly influence equity markets. In this paper, we are
interested in predicting the short-term movement of stock prices after
financial news events using only the headlines of the news. To achieve this
goal, we introduce a new text mining method called Fine-Tuned
Contextualized-Embedding Recurrent Neural Network (FT-CE-RNN). Compared with
previous approaches which use static vector representations of the news (static
embedding), our model uses contextualized vector representations of the
headlines (contextualized embeddings) generated from Bidirectional Encoder
Representations from Transformers (BERT). Our model obtains the
state-of-the-art result on this stock movement prediction task. It shows
significant improvement compared with other baseline models, in both accuracy
and trading simulations. Through various trading simulations based on millions
of headlines from Bloomberg News, we demonstrate the ability of this model in
real scenarios.
