Financial sentiment analysis misses critical predictive signals; extracting structured semantic dimensions (event type, impact scope, temporal horizon, confidence) alongside sentiment improves stock prediction accuracy and reveals that sentiment-return relationships are highly nonlinear.
This paper shows that financial news contains multiple independent information dimensions beyond sentiment—like event type, impact scope, and time horizon—that together predict stock movements better than sentiment alone.