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This paper introduces a persona-based index designed to predict the U.S. Federal Open Market Committee's decisions on interest rates by leveraging a dataset of nearly 25,000 data chunks to create distinct personas that reflect varied monetary policy stances. The personas exhibit high identifiability and detectability, with their query-conditioned representations significantly outperforming traditional retrieval methods in capturing the dynamics of monetary policy. The resulting index not only tracks the interest rate cycle with high accuracy but also leads the federal funds target rate by approximately three quarters, marking a novel approach to understanding group behavior in economic contexts.
Capturing the nuanced dynamics of monetary policy through digital personas, this index predicts FOMC rate decisions with remarkable accuracy, outperforming traditional models.
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 ($\hat\tau_{\mathrm{det}} = 0.23$ 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 $\tau = 0.63$, $p<0.001$), 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 $\tau = 0.68$, $p<10^{-6}$) 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.
