用历史新闻提升大模型对财经新闻的市场影响预测能力
Context-Aware Language Models for Forecasting Market Impact from Sequences of Financial News
- 大模型主处理新闻，小模型压缩历史上下文为嵌入向量
- 引入历史上下文使模型预测准确率显著提升，跨时间跨度有效
- 可直接用于模拟投资策略，提升真实交易表现
财经新闻在金融市场信息传播中起关键作用，是股价变动的重要驱动因素。然而单篇新闻内容常不自洽，需结合历史新闻背景才能准确解读。本文研究大型语言模型在理解新闻市场影响时，历史上下文的价值。实验表明，历史上下文能持续且显著提升不同方法与时间尺度下的性能。为此，我们提出一种高效方法：由大模型处理主新闻，小模型将历史新闻编码为简洁摘要嵌入，并对齐至大模型表示空间。通过多维度可解释性分析，揭示了上下文融合的机制价值。最终验证，该方法在模拟投资中带来实质性绩效提升。
原文摘要 · Abstract (English)
Financial news plays a critical role in the information diffusion process in financial markets and is a known driver of stock prices. However, the information in each news article is not necessarily self-contained, often requiring a broader understanding of the historical news coverage for accurate interpretation. Further, identifying and incorporating the most relevant contextual information presents significant challenges. In this work, we explore the value of historical context in the ability of large language models to understand the market impact of financial news. We find that historical context provides a consistent and significant improvement in performance across methods and time horizons. To this end, we propose an efficient and effective contextualization method that uses a large LM to process the main article, while a small LM encodes the historical context into concise summary embeddings that are then aligned with the large model's representation space. We explore the behavior of the model through multiple qualitative and quantitative interpretability tests and reveal insights into the value of contextualization. Finally, we demonstrate that the value of historical context in model predictions has real-world applications, translating to substantial improvements in simulated investment performance.
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