{"slug": "beyond-sentiment-structured-information-extraction-from-financial-news", "title": "Beyond Sentiment: Structured Information Extraction from Financial News", "summary": "Researchers from an unnamed institution proposed a structured information extraction framework using LLaMA-3.1-70B to extract six semantic dimensions from financial news, finding that combining these with FinBERT sentiment features improves stock movement prediction F1 to 0.600, significantly outperforming sentiment alone (p < 0.0001). The study, based on 41,618 news–stock pairs from the FNSPID dataset, shows that non-sentiment dimensions contribute an additional ΔF1 = +0.019 over FinBERT alone, indicating that sentiment-only analysis incurs substantial information loss.", "body_md": "arXiv:2607.28496v1 Announce Type: new\nAbstract: 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 orthogonal information dimensions---event type, impact scope, temporal horizon, and semantic confidence---that sentiment alone cannot capture, and that these dimensions carry independent predictive value. To test this hypothesis, we propose a structured information extraction framework that leverages LLaMA-3.1-70B to extract six semantic dimensions from financial news. Through large-scale experiments on 41,618 news--stock pairs from the FNSPID dataset, we find that (i) FinBERT sentiment features exhibit strong predictive power under nonlinear models (F1=0.576) but substantially weaker performance under linear models (F1=0.230), revealing a highly nonlinear sentiment--return relationship; (ii) LLM-extracted structured features, while individually weaker, capture information orthogonal to sentiment, as evidenced by a 53.5% systematic disagreement rate between the two approaches; and (iii) combining both signal sources yields F1=0.600, significantly outperforming either alone ($p < 0.0001$), with consistent improvements across all seven event types. Ablation experiments confirm that non-sentiment structural dimensions (event type, impact subject, time horizon, confidence) independently contribute $\\Delta\\text{F1} = +0.019$ beyond FinBERT alone. Feature importance analysis reveals balanced contributions from all six extracted dimensions (14--21%), demonstrating that compressing news into a single sentiment score incurs substantial information loss. Our results suggest that the sentiment--semantics decoupling in financial text is systematic and exploitable, opening a new direction for multi-dimensional financial NLP.", "url": "https://wpnews.pro/news/beyond-sentiment-structured-information-extraction-from-financial-news", "canonical_source": "https://www.machinebrief.com/news/beyond-sentiment-structured-information-extraction-from-fina-0s45", "published_at": "2026-07-31 04:00:00+00:00", "updated_at": "2026-07-31 06:33:29.302341+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "natural-language-processing"], "entities": ["LLaMA-3.1-70B", "FinBERT", "FNSPID"], "alternates": {"html": "https://wpnews.pro/news/beyond-sentiment-structured-information-extraction-from-financial-news", "markdown": "https://wpnews.pro/news/beyond-sentiment-structured-information-extraction-from-financial-news.md", "text": "https://wpnews.pro/news/beyond-sentiment-structured-information-extraction-from-financial-news.txt", "jsonld": "https://wpnews.pro/news/beyond-sentiment-structured-information-extraction-from-financial-news.jsonld"}}