FedSight AI: Multi-Agent System Architecture for Federal Funds Target Rate Prediction

TL;DR

FedSight AI employs a multi-agent LLM-based framework to simulate FOMC deliberations, accurately predicting federal funds rate decisions with transparent reasoning and improved efficiency, outperforming existing baselines.

Contribution

This paper introduces FedSight AI, a novel multi-agent system utilizing large language models to replicate FOMC decision-making and enhance prediction accuracy.

Findings

Achieved 93.75% accuracy in predicting FOMC decisions.

Demonstrated improved stability over baseline models.

Provided transparent reasoning aligned with actual FOMC communications.

Abstract

The Federal Open Market Committee (FOMC) sets the federal funds rate, shaping monetary policy and the broader economy. We introduce \emph{FedSight AI}, a multi-agent framework that uses large language models (LLMs) to simulate FOMC deliberations and predict policy outcomes. Member agents analyze structured indicators and unstructured inputs such as the Beige Book, debate options, and vote, replicating committee reasoning. A Chain-of-Draft (CoD) extension further improves efficiency and accuracy by enforcing concise multistage reasoning. Evaluated at 2023-2024 meetings, FedSight CoD achieved accuracy of 93.75\% and stability of 93.33\%, outperforming baselines including MiniFed and Ordinal Random Forest (RF), while offering transparent reasoning aligned with real FOMC communications.