用事件反思双层记忆，提升股市预测的可解释性。
StockMem: An Event-Reflection Memory Framework for Stock Forecasting
- 将新闻拆解为事件，分横向整合与纵向追踪两个维度建模
- 在真实数据上实现比现有方法更高的预测准确率
- 适合需要透明决策依据的量化交易与金融分析师
股价预测因市场波动性和对实时事件的敏感性而极具挑战。尽管大语言模型为文本驱动预测提供了新路径，但其在金融领域的应用受限于新闻数据噪声及文本中缺乏明确答案。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StockMem，一种事件-反思双层记忆框架。该框架将新闻结构化为事件，并沿两个维度挖掘：水平整合用于融合每日事件，纵向追踪捕捉事件演化，以提取反映市场预期差异的增量信息，构建时间序列事件知识库；通过分析事件-价格动态，进一步形成因果经验反思知识库。预测时，框架检索历史相似场景，结合当前事件、增量数据与过往经验进行推理。实验表明，StockMem优于现有记忆架构，在真实数据上表现更优，且能追踪影响价格的信息链，增强金融预测的可解释性与决策透明度。
原文摘要 · Abstract (English)
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.
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