用大模型分析巴西央行货币政策声明的语气，区分鹰派鸽派与政策信号。
Reading Copom's Tone: A Weighted LLM Framework for Hawkish-Dovish Sentiment, Forward Guidance, and Uncertainty
- 通过大模型识别句子语气强度，赋予0到1的权重并加权汇总成-1到1得分
- 80份声明中33.3%为鹰派，平均文档得分+0.107，最高达+0.570（2021年8月）
- 分离语气、前瞻性指引与不确定性，适合关注央行政策意图的研究者
本文构建了一个用于衡量巴西货币委员会（Copom）政策声明语气的自然语言处理框架。该框架受iSent启发，将官方文本分句为鹰派、鸽派、中性及无关四类，并在三方面进行扩展：首先，利用大模型识别短语级鹰派/鸽派表达，并赋予0至1的强度权重；其次，文档指数结合句数与平均信号强度，生成-1至1的有界评分；第三，另设全文档层评估前瞻性指引方向、明确度、不确定性水平及其变化。样本涵盖2016年8月后共80份声明，包含1,498个已分类句子（截至2026年8月5日）。其中33.3%为鹰派，18.0%为鸽派，42.1%为中性，6.5%为无关。平均文档得分为+0.107，最鹰派记录为+0.570（2021年8月）。最新声明（2026年8月5日）得分为+0.232，含8句鹰派、2句鸽派、9句中性，其结构分析显示指引方向模糊但部分明确，不确定性处于中等且高于上次会议。语气与指引方向得分的相关系数为0.719。结果为描述性输出，不预测Selic利率或债券收益，核心贡献在于建立一个透明、可审计、可分项的量化系统。
原文摘要 · Abstract (English)
This paper documents an applied natural-language-processing framework for measuring the tone of Brazilian Monetary Policy Committee (Copom) statements. The project is explicitly inspired by iSent, Itaú's Central Bank sentiment classifier, particularly its sentence-level division of official communication into hawkish, dovish, neutral, and out-of-context classes. The implementation extends that idea in three directions. First, an LLM identifies short hawkish and dovish expressions and assigns each a 0-to-1 intensity weight. Second, the document index combines sentence counts with document-specific average signal intensities, producing a bounded score from -1 to 1. Third, a separate full-document layer measures forward-guidance direction, guidance explicitness, uncertainty level, and change in uncertainty. The empirical sample is restricted to communications dated August 2016 or later and contains 80 statements and 1,498 classified sentences from August 31, 2016 through August 5, 2026. Across this sample, 33.3% of sentences are hawkish, 18.0% dovish, 42.1% neutral, and 6.5% out of context. The average document score is +0.107, while the most hawkish reading is +0.570 in August 2021. The latest statement, dated August 5, 2026, scores +0.232, with eight hawkish, two dovish, and nine neutral sentences. Its structural overlay is more nuanced: guidance is directionally ambiguous but partly explicit, while uncertainty is classified as central and higher than at the prior meeting. Tone and the guidance-direction score have a contemporaneous Pearson correlation of 0.719. These are descriptive outputs, not a validated forecast of Selic decisions or DI returns. The main contribution is therefore methodological: a transparent, incremental, auditable system that separates rhetorical tone from policy guidance and uncertainty.
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