FinBERT

Structured LLM extraction of financial news outperforms sentiment-only approaches for stock prediction

Researchers propose a framework using LLaMA-3.1-70B to extract six semantic dimensions from financial news (event type, impact scope, temporal horizon, semantic confidence, etc.) beyond simple sentiment polarity. Experiments on 41,618 news-stock pairs from the FNSPID dataset show that combining LLM-extracted structured features with FinBERT sentiment achieves F1=0.600, significantly outperforming either alone, with a 53.5% systematic disagreement rate indicating the two signal sources are largely orthogonal. The work argues that compressing financial news to a single sentiment score incurs substantial information loss and that multi-dimensional NLP extraction is systematically exploitable for prediction tasks.