Paper: arXiv 2508.07408

Authors: Yueyi Wang, Qiyao Wei

Abstract

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.

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 7.2/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper focuses on practical implementation of LLM-based feature engineering with extensive backtesting metrics (Sharpe ratios, information coefficients) and transparency, but its mathematical depth is moderate, relying primarily on statistical testing rather than advanced derivations.

Research Flowchart

  flowchart TD
  A["Research Goal:<br/>Extract Event-Aware Sentiment<br/>Alpha from Financial Tweets"] --> B["Input: Company-Related<br/>Twitter Data Corpus"]
  B --> C["LLM-Augmented<br/>Semantic Annotation<br/>Assign Multi-Label Events"]
  C --> D["Signal Alignment:<br/>Link Sentiment to<br/>1-7 Day Forward Returns"]
  D --> E{"Statistical &<br/>Tradability Evaluation"}
  E -->|Quantitative Metrics| F["Key Findings/Outcomes"]
  F --> G["Identified Negative Alpha<br/>Event Labels (Sharpe ~ -0.38)"]
  F --> H["Transparent Framework<br/>Code & Methods Publicly Available"]