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

翻译：金融新闻能否可靠地预测短期股票走势？尽管大语言模型取得了进展，这一问题仍未得到解决。我们利用零样本自然语言处理框架重新审视该问题，探究模型是否能从金融新闻中提取可操作信号而无需领域特定训练。我们设计了一个结构化流水线，将零样本自然语言推理与时序聚合相结合，在整合跨文章信息时明确建模时效性和事件依赖的影响时间窗口。为解决高风险场景中对透明度的需求，我们引入了一个多层可解释性框架，该框架将预测结果与词元级、文章级及聚合级证据关联起来，并生成基于文本的自然语言理由。在多种模型和预测时间窗口下，我们发现零样本方法始终未能超越简单基线，尤其在负向波动上表现尤为薄弱，这表明将新闻情感映射至短期价格动态存在更深层的结构性局限。然而，可解释性信号能可靠区分可信与不可信的预测，即使在准确率有限的情况下仍具有实用价值。这些发现揭示了零样本金融NLP的局限性，并推动研究转向优先考虑透明度与不确定性感知的决策支持系统。代码：https://github.com/alimert05/zero-shot-stock-xai