Stock market predictions lend themselves well to a machine learning framework due to their quantitative nature. A supervised learning model to predict stock movement direction can combine technical information and qualitative sentiment through news, encoded into fixed length real vectors. We attempt a large range of models, both to encode qualitative sentiment information into features, and to make a final up or down prediction on the direction of a particular stock given encoded news and technical features. We find that a Universal Sentence Encoder, combined with SVMs, achieve encouraging results on our data.
- scikit-learn
pip install sklearn - pytorch
pip install pytorch - keras
pip install keras - tensorflow
pip install tensorflow - tensorflow hub
pip install tensorflow-hub