Can News Predict the Market? Limits of Zero-Shot Financial NLP and the Role of Explainable AI

Zero-shot LLMs fail to beat baselines on stock prediction; explainability signals retain practical value

A new arXiv preprint evaluates zero-shot NLP pipelines for predicting short-term stock movements from financial news, finding that across multiple models and prediction horizons, zero-shot approaches consistently fail to outperform simple baselines, with especially weak performance on negative price movements. The authors introduce a multi-layered explainability framework linking predictions to token-, article-, and aggregate-level evidence, finding that explainability signals can reliably distinguish trustworthy from unreliable predictions even when accuracy is low. The work argues for a shift toward decision-support systems emphasizing transparency and uncertainty awareness rather than raw predictive accuracy.