# Event-Aware Sentiment Factors from LLM-Augmented Financial Tweets: A Transparent Framework for Interpretable Quant Trading

arXiv: https://arxiv.org/abs/2508.07408 · HTML: https://mlsanity.com/paper/2508.07408/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 10 references that are in the map.
- First posted on arXiv 10 Aug 2025.

## Builds on (most cited first, 3 of 3)

- [The Effects of Twitter Sentiment on Stock Price Returns](https://mlsanity.com/paper/1506.02431/) (2015)
- [Pre-trained Large Language Models for Financial Sentiment Analysis](https://mlsanity.com/paper/2401.05215/) (2024)
- [A Tweet-based Dataset for Company-Level Stock Return Prediction](https://mlsanity.com/paper/2006.09723/) (2020)

## Abstract (arXiv)

In this study, we wish to showcase the unique utility of large language models (LLMs) in financial semantic annotation and alpha signal discovery. Leveraging a corpus of company-related tweets, we use an LLM to automatically assign multi-label event categories to high-sentiment-intensity tweets. We align these labeled sentiment signals with forward returns over 1-to-7-day horizons to evaluate their statistical efficacy and market tradability. Our experiments reveal that certain event labels consistently yield negative alpha, with Sharpe ratios as low as -0.38 and information coefficients exceeding 0.05, all statistically significant at the 95% confidence level. This study establishes the feasibility of transforming unstructured social media text into structured, multi-label event variables. A key contribution of this work is its commitment to transparency and reproducibility; all code and methodologies are made publicly available. Our results provide compelling evidence that social media sentiment is a valuable, albeit noisy, signal in financial forecasting and underscore the potential of open-source frameworks to democratize algorithmic trading research.