A Persona-based Rate Action Index

2026-07-29

AI Summary

This study proposes a novel approach based on digital personality modeling to predict Federal Open Market Committee (FOMC) interest rate decisions—specifically, rate hikes, holds, or cuts. By constructing individualized corpora for each FOMC member and integrating retrieval-augmented generation with a personalized response mechanism, the method dynamically captures the temporal evolution of their monetary policy stances. This work presents the first interpretable model of FOMC collective behavior and yields a predictive index that leads the policy rate by approximately three quarters. Evaluated over the 2022–2025 sample period, the index exhibits strong alignment with actual rate cycles (Kendall’s τ = 0.68) and achieves a classification accuracy of 0.69, significantly outperforming benchmark models (0.47).

Abstract

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.

Problem

• monetary policy
• FOMC
• rate prediction
• digital personas
• federal funds rate

Innovation

• persona-based modeling
• monetary policy prediction
• retrieval-augmented generation
• time-varying behavioral representation
• FOMC decision forecasting