NASA’s new dark energy space telescope can also detect killer asteroids
technologyreview.com·3h ago
AI Research
arXiv CS.AI·May 28·8 min read
FinTexTS: Financial Text-Paired Time-Series Dataset via Semantic-Based and Multi-Level Pairing
TL;DR
Financial machine learning models struggle to leverage both time-series price data and related news text simultaneously, limiting prediction accuracy. FinTexTS dataset pairs financial news articles with stock price movements across multiple semantic and temporal alignment levels.
✦ Why It Matters
Engineers can build more accurate financial forecasting models by training on paired text-price data instead of isolated modalities.
Key Takeaways
- Utilize FinTexTS for enhanced financial data analysis.
- Implement semantic-based pairing for better context understanding.
- Explore multi-level classification of news articles.
- Leverage proprietary news sources for improved forecasting.
How It Works
The framework begins by extracting company-specific context from SEC filings, which provides a foundation for understanding the target company's situation. It then employs an embedding-based matching mechanism to identify relevant news articles that align semantically with this context.
By classifying news into four distinct levels, the framework captures the influence of broader economic factors, sector trends, and specific company events on stock prices.