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

By PulseAugur Editorial · [1 sources] ·

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]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

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

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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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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Daohan Zhu, Sitong Ge, Ruofei Wang, Honggu Chen, Yubo Hou, Tao Wan, Zengchang Qin ·

    Beyond Sentiment: Structured Information Extraction from Financial News

    arXiv:2607.28496v1 Announce Type: new Abstract: Financial sentiment analysis has become a standard component in news-driven stock prediction, yet it reduces rich, multi-dimensional news articles to a single polarity score. We hypothesize that financial news encodes multiple ortho…

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