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FinTexTS: Financial Text-Paired Time-Series Dataset via Semantic-Based and Multi-Level Pairing

arXiv – CS AI|Jaehoon Lee, Suhwan Park, Tae Yoon Lim, Seunghan Lee, Jun Seo, Dongwan Kang, Hwanil Choi, Minjae Kim, Sungdong Yoo, SoonYoung Lee, Yongjae Lee, Wonbin Ahn|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers have developed FinTexTS, a new large-scale dataset that pairs financial news with stock price data using semantic matching and multi-level categorization. The framework uses embedding-based matching and LLMs to classify news into four levels (macro, sector, related company, and target company) for improved stock price forecasting accuracy.

Key Takeaways

→FinTexTS introduces a semantic-based approach to pair financial news with time-series data, moving beyond simple keyword matching.
→The framework classifies news into four hierarchical levels to capture complex market interdependencies affecting stock prices.
→Large language models are used to categorize news articles and improve the relevance of text-financial data pairing.
→Experimental results demonstrate improved stock price forecasting performance using this multi-level semantic approach.
→The method shows even better results when applied to proprietary, curated news sources compared to publicly available data.

#fintech #machine-learning #financial-data #stock-prediction #semantic-analysis #llm #time-series #financial-modeling

Read Original →via arXiv – CS AI

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