# News-Driven Stock Prediction With Attention-Based Noisy Recurrent State Transition

arXiv: https://arxiv.org/abs/2004.01878 · HTML: https://mlsanity.com/paper/2004.01878/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2020.

## Built on by (most cited first, 2 of 2)

- [Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey](https://mlsanity.com/paper/2308.04947/) (2023)
- [Deep Multiple Instance Learning For Forecasting Stock Trends Using Financial News](https://mlsanity.com/paper/2206.14452/) (2022)

## Abstract (arXiv)

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.