LLaMA-3.1-70B extracts structured data from financial news to boost stock prediction

Researchers have developed a new framework for extracting structured information from financial news, moving beyond traditional sentiment analysis. This framework utilizes LLaMA-3.1-70B to identify six semantic dimensions, including event type, impact scope, temporal horizon, and semantic confidence. Experiments on the FNSPID dataset demonstrated that these structured features, when combined with sentiment analysis, significantly improve stock prediction accuracy compared to using sentiment alone. AI

IMPACT This research could lead to more sophisticated AI-driven financial analysis tools by extracting richer information than sentiment alone.

RANK_REASON The item is an academic paper detailing a new framework and experimental results for structured information extraction from financial news. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• FinBERT
• FNSPID
• Gotit.pub
• Hugging Face
• Llama 3.1 70B
• ScienceCast

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