发表机构
Helivan; Wavelength Capital（赫利万; 波长资本）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究构建基于角色的利率行动指数，通过数字角色捕捉美联储成员货币政策立场，能追踪利率周期、预测FOMC会议利率决策，领先联邦基金目标利率约三个季度。
AI 中文摘要
我们提出了一种基于一组角色对当前市场条件的反应来预测美国联邦公开市场委员会（FOMC）加息、维持或下调当前联邦基金目标利率决策的指数。为构建该指数，我们从公开数据中收集了一个包含近25000个可检索片段的新数据集，将数据划分为每位成员的语料库，并将每个语料库用作我们全程称为“角色”的生成系统的检索数据库。我们首先在两个互补的相似性组成部分上评估这些角色：可识别性和可检测性。每个角色的行为具有高度可归因性（平均成员条件召回率为随机概率的8倍），且生成内容与保留的真实内容几乎无法区分（检测指标τ̂_det=0.23，而基线为0.15）。随后我们提供证据表明，角色的查询条件表示能捕捉成员相对于已知鹰鸽声誉排序的货币政策立场（肯德尔τ=0.63，p<0.001），其表现显著优于仅基于检索的表示。这些表示随时间和当前市场条件变化，构成了我们提出的基于角色的利率行动指数的基础。对于2022至2025年期间，该指数追踪利率周期（肯德尔τ=0.68，p<10⁻⁶），可用于构建简单分类器，以非平凡的准确率（0.69，而基线率为0.47）预测每次会议的结果。重要的是，该指数优于信息基准，且领先联邦基金目标利率约三个季度。据我们所知，我们的结果首次证明了通过一组数字角色捕捉时变群体行为的能力。
英文摘要
We propose an index for predicting the U.S.\ Federal Open Market Committee (FOMC) decision to hike/hold/cut the current federal funds target rate based on how a collection of personas responds to current market conditions. To construct the index, we collected a new dataset consisting of nearly $25{,}000$ retrievable chunks from publicly available data. We partition the data into per-member corpora and use each as the retrieval database of a generative system we refer to throughout as a ``persona''. We first evaluate the personas across two complementary components of likeness: identifiability and detectability. Each persona's behavior is highly attributable (average member-conditional recall is $ 8\times $ chance) and generated content is nearly indistinguishable from held-out real content ($\hatτ_{\mathrm{det}} = 0.23$ against a $0.15$ floor). We then present evidence that query-conditioned representations of the personas capture members' monetary-policy stance relative to a known hawk--dove reputational ordering (Kendall's $τ= 0.63$, $p < 0.001$), substantially outperforming retrieval-only representations. These representations vary with time and current market conditions and form the basis of our proposed persona-based rate action index. For the $2022$--$2025$ period the index tracks the rate cycle (Kendall's $τ= 0.68$, $p < 10^{-6}$) and can be used to construct a simple classifier that predicts per-meeting outcomes at non-trivial accuracy ($0.69$ versus a $0.47$ base rate). Importantly, the index outperforms informative baselines and leads the federal funds target rate by roughly three quarters. As far as we are aware, our results are the first to demonstrate the ability to capture time-varying group behavior via a collection of digital personas.
