Stock price prediction is challenging due to market volatility and its sensitivity to real-time events. While large language models (LLMs) offer new avenues for text-based forecasting, their application in finance is hindered by noisy news data and the lack of explicit answers in text. General-purpose memory architectures struggle to identify the key drivers of price movements. To address this, we propose StockMem, an event-reflection dual-layer memory framework. It structures news into events and mines them along two dimensions: horizontal consolidation integrates daily events, while longitudinal tracking captures event evolution to extract incremental information reflecting market expectation discrepancies. This builds a temporal event knowledge base. By analyzing event-price dynamics, the framework further forms a reflection knowledge base of causal experiences. For prediction, it retrieves analogous historical scenarios and reasons with current events, incremental data, and past experiences. Experiments show StockMem outperforms existing memory architectures and provides superior, explainable reasoning by tracing the information chain affecting prices, enhancing decision transparency in financial forecasting.

翻译：股票价格预测因市场波动性及其对实时事件的敏感性而极具挑战。尽管大型语言模型（LLMs）为基于文本的预测提供了新途径，但其在金融领域的应用受到新闻数据噪声以及文本中缺乏明确答案的制约。通用记忆架构难以识别价格变动的关键驱动因素。为此，我们提出了StockMem，一种事件-反思双层记忆框架。该框架将新闻组织为事件，并从两个维度进行挖掘：横向整合集成每日事件，而纵向追踪则捕捉事件演变以提取反映市场预期差异的增量信息，从而构建时序事件知识库。通过分析事件-价格动态，框架进一步形成因果经验的反思知识库。在预测时，它检索类似的历史情境，并结合当前事件、增量数据及过往经验进行推理。实验表明，StockMem在性能上优于现有记忆架构，并通过追溯影响价格的信息链提供更优且可解释的推理，增强了金融预测的决策透明度。