# Learning Explainable Stock Predictions with Tweets Using Mixture of Experts

arXiv: https://arxiv.org/abs/2507.20535 · HTML: https://mlsanity.com/paper/2507.20535/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2025.

## Built on by (most cited first, 3 of 3)

- [PromptSculptor: Multi-Agent Based Text-to-Image Prompt Optimization](https://mlsanity.com/paper/2509.12446/) (2025)
- [Coarse-to-Fine Personalized LLM Impressions for Streamlined Radiology Reports](https://mlsanity.com/paper/2508.15845/) (2025)
- [TOFA: Training-Free One-Shot Federated Adaptation for Vision-Language Models](https://mlsanity.com/paper/2511.16423/) (2025)

## Abstract (arXiv)

Stock price movements are influenced by many factors, and alongside historical price data, tex-tual information is a key source. Public news and social media offer valuable insights into market sentiment and emerging events. These sources are fast-paced, diverse, and significantly impact future stock trends. Recently, LLMs have enhanced financial analysis, but prompt-based methods still have limitations, such as input length restrictions and difficulties in predicting sequences of varying lengths. Additionally, most models rely on dense computational layers, which are resource-intensive. To address these challenges, we propose the FTS- Text-MoE model, which combines numerical data with key summaries from news and tweets using point embeddings, boosting prediction accuracy through the integration of factual textual data. The model uses a Mixture of Experts (MoE) Transformer decoder to process both data types. By activating only a subset of model parameters, it reduces computational costs. Furthermore, the model features multi-resolution prediction heads, enabling flexible forecasting of financial time series at different scales. Experimental results show that FTS-Text-MoE outperforms baseline methods in terms of investment returns and Sharpe ratio, demonstrating its superior accuracy and ability to predict future market trends.