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

• URL: http://arxiv.org/abs/2606.12210v1
• Date: Wed, 10 Jun 2026 15:28:10 GMT
• Title: Can News Predict the Market? Limits of Zero-Shot Financial NLP and the Role of Explainable AI
• Abstract summary: We investigate whether models can extract actionable signals from financial news without domain-specific training.<n>We design a structured pipeline that combines zero-shot natural language inference with temporal aggregation.<n>To address the need for transparency in high-stakes settings, we introduce a multi-layered explainability framework.
• Score: 11.542407696902115
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Can financial news reliably predict short-term stock movements? Despite advances in large language models, this question remains unresolved. We revisit this problem using a zero-shot natural language processing framework, investigating whether models can extract actionable signals from financial news without domain-specific training. We design a structured pipeline that combines zero-shot natural language inference with temporal aggregation, explicitly modelling recency and event-dependent impact horizons when integrating information across articles. To address the need for transparency in high-stakes settings, we introduce a multi-layered explainability framework that links predictions to token-level, article-level, and aggregate evidence, and produces grounded natural language rationales. Across multiple models and prediction horizons, we find that zero-shot approaches consistently fail to outperform simple baselines, with particularly weak performance on negative movements, suggesting deeper structural limitations in mapping news sentiment to short-term price dynamics. However, explainability signals reliably distinguish between trustworthy and unreliable predictions, offering practical value even when accuracy is limited. These findings highlight the limits of zero-shot financial NLP and motivate a shift toward decision-support systems that prioritise transparency and uncertainty awareness. Code: https://github.com/alimert05/zero-shot-stock-xai

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