News-Driven Stock Prediction With Attention-Based Noisy Recurrent State Transition
This work addresses stock prediction for investors by explicitly modeling events and noise, though it appears incremental in its approach.
The paper tackled stock movement prediction by modeling underlying stock value sequences with a recurrent state transition model that separates news effects from noise, achieving improved performance over strong baselines.
We consider direct modeling of underlying stock value movement sequences over time in the news-driven stock movement prediction. A recurrent state transition model is constructed, which better captures a gradual process of stock movement continuously by modeling the correlation between past and future price movements. By separating the effects of news and noise, a noisy random factor is also explicitly fitted based on the recurrent states. Results show that the proposed model outperforms strong baselines. Thanks to the use of attention over news events, our model is also more explainable. To our knowledge, we are the first to explicitly model both events and noise over a fundamental stock value state for news-driven stock movement prediction.