利用新闻衍生的货币政策信号预测日内美元/加元汇率
Forecasting Intraday USD/CAD Exchange Rate with News-Derived Monetary-Policy Signals
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中文总结 AI 辅助
本文提出统计归因方法，利用大语言模型将货币政策新闻转化为结构化信号，通过滚动窗口实验证明沟通时机等维度对日内美元/加元汇率具有可测量的预测价值，且归因分析优于仅关注整体准确性。
中文摘要 AI 辅助
货币政策公告和央行沟通在外汇市场中发挥着核心作用，但其定性、非结构化的形式使得其预测价值难以量化。虽然先前的研究主要集中于从金融新闻中提取情绪，但关于货币政策沟通不同维度的相对贡献知之甚少。现有研究主要评估文本信息是否能提高整体预测性能，但对哪些沟通渠道推动了这种改进提供的见解有限。为解决这一空白，本文提出了一种统计归因方法，将货币政策沟通分解为可解释的渠道，并在错误发现率控制下量化其增量预测贡献。货币政策新闻通过大型语言模型（LLMs）和时间特征工程转化为结构化的沟通信号。这些信号使用基于树的机器学习模型进行滚动窗口实验评估。结果表明，货币政策沟通包含可测量的预测信息。归因分析显示，预测价值集中在一小部分信号中，其中沟通时机提供了最强的个体特征级贡献，针对性的沟通活动度量也产生正向贡献，而LLM衍生的情绪在组级提供了补充信息。研究结果表明，基于沟通的预测价值不仅限于情绪，归因而非仅聚合准确性，对于评估新闻衍生的信号至关重要。
英文摘要
Monetary-policy announcements and central-bank communications play a central role in foreign exchange markets, yet their qualitative, unstructured form makes their forecasting value difficult to quantify. While prior research has largely focused on sentiment extracted from financial news, comparatively little is known about the relative contribution of different dimensions of monetary-policy communication. Existing studies primarily evaluate whether textual information improves overall forecasting performance but provide limited insight into which communication channels drive such improvements. To address this gap, this paper introduces a statistical attribution methodology that decomposes monetary-policy communication into interpretable channels and quantifies their incremental forecasting contribution under false-discovery-rate control. Monetary-policy news is transformed into structured communication signals using large language models (LLMs) and temporal feature engineering. These signals are evaluated using rolling-window experiments with tree-based machine-learning models. The results show that monetary-policy communication contains measurable predictive information. Attribution analysis shows that predictive value is concentrated in a small subset of signals, with communication timing providing the strongest individual feature-level contribution, targeted communication-activity measures also contributing positively, and LLM-derived sentiment providing complementary information at the group level. The findings indicate that communication-based forecasting value extends beyond sentiment alone and that attribution, rather than aggregate accuracy alone, is central to evaluating news-derived signals.
