# Dynamic and Context-Dependent Stock Price Prediction Using Attention Modules and News Sentiment

arXiv: https://arxiv.org/abs/2205.01639 · HTML: https://mlsanity.com/paper/2205.01639/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 37 references that are in the map.
- First posted on arXiv 13 Mar 2022.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Neural Relational Inference for Interacting Systems](https://mlsanity.com/paper/1802.04687/) (2018)
- [Recurrent Independent Mechanisms](https://mlsanity.com/paper/1909.10893/) (2019)
- [DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning](https://mlsanity.com/paper/2106.03760/) (2021)
- [The Consciousness Prior](https://mlsanity.com/paper/1709.08568/) (2017)
- [Attention in Natural Language Processing](https://mlsanity.com/paper/1902.02181/) (2019)
- [Relational recurrent neural networks](https://mlsanity.com/paper/1806.01822/) (2018)
- [Tracking the World State with Recurrent Entity Networks](https://mlsanity.com/paper/1612.03969/) (2016)
- [Describing Multimedia Content using Attention-based Encoder--Decoder Networks](https://mlsanity.com/paper/1507.01053/) (2015)
- [Financial series prediction using Attention LSTM](https://mlsanity.com/paper/1902.10877/) (2019)
- [Industrial Forecasting with Exponentially Smoothed Recurrent Neural Networks](https://mlsanity.com/paper/2004.04717/) (2020)

## Built on by (most cited first, 1 of 1)

- [AgensFlow: A Coordination-Policy Substrate for Multi-Agent Systems](https://mlsanity.com/paper/2605.27466/) (2026)

## Abstract (arXiv)

The growth of machine-readable data in finance, such as alternative data, requires new modeling techniques that can handle non-stationary and non-parametric data. Due to the underlying causal dependence and the size and complexity of the data, we propose a new modeling approach for financial time series data, the αt-RIM (recurrent independent mechanism). This architecture makes use of key-value attention to integrate top-down and bottom-up information in a context-dependent and dynamic way. To model the data in such a dynamic manner, the αt-RIM utilizes an exponentially smoothed recurrent neural network, which can model non-stationary times series data, combined with a modular and independent recurrent structure. We apply our approach to the closing prices of three selected stocks of the S&P 500 universe as well as their news sentiment score. The results suggest that the αt-RIM is capable of reflecting the causal structure between stock prices and news sentiment, as well as the seasonality and trends. Consequently, this modeling approach markedly improves the generalization performance, that is, the prediction of unseen data, and outperforms state-of-the-art networks such as long short-term memory models.