Stock Movement Prediction with Financial News using Contextualized Embedding from BERT
This work addresses stock movement prediction for investors using a novel method, but it is incremental as it builds on existing BERT and RNN techniques for a specific domain.
The paper tackled predicting short-term stock price movements using financial news headlines by introducing a Fine-Tuned Contextualized-Embedding Recurrent Neural Network (FT-CE-RNN) that leverages BERT for contextualized embeddings, achieving state-of-the-art results with significant improvements in accuracy and trading simulations based on millions of headlines.
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.