# Bridging the Gap Between Natural Language and Market Dynamics via High-Dimensional Representation Learning

arXiv: https://arxiv.org/abs/2605.30652 · HTML: https://mlsanity.com/paper/2605.30652/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 28 May 2026.

## Builds on (most cited first, 5 of 5)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Predicting Stock Prices with FinBERT-LSTM: Integrating News Sentiment Analysis](https://mlsanity.com/paper/2407.16150/) (2024)
- [Financial Sentiment Analysis on News and Reports Using Large Language Models and FinBERT](https://mlsanity.com/paper/2410.01987/) (2024)
- [Fine-Tuning Large Language Models for Stock Return Prediction Using Newsflow](https://mlsanity.com/paper/2407.18103/) (2024)
- [Contrastive Similarity Learning for Market Forecasting: The ContraSim Framework](https://mlsanity.com/paper/2502.16023/) (2025)

## Abstract (arXiv)

Traditional multi-modal financial forecasting often relies on scalar sentiment scores, which fail to capture the nuances of financial news. To address this information loss, this paper explores high-dimensional representation learning by replacing discrete polarity ratings with dense FinBERT embeddings within a Transformer-based forecasting architecture. We benchmarked various embedding strategies on the FNSPID dataset, including raw embeddings, attention-weighted aggregation, and a custom Siamese network. While the attention-based mechanism struggled with the low signal-to-noise ratio typical of financial data, the integration of Siamese-optimized embeddings outperformed both the scalar baseline and raw embedding approaches, demonstrating that preserving high-dimensional narrative context yields improved predictive accuracy for short-term stock price movements.