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We present the application of Large Language Models (LLMs) to perform sentiment analysis on the United States Federal Reserve Beige Books. These reports are a critical qualitative resource for understanding the economic conditions in the United States and are instrumental in the decision-making of the Federal Reserve. Several past analyses have focused on the economic impact of the Beige Books. We present a novel approach by quantifying the report sentiment using recent sentiment analysis techniques. Our findings show that certain sections of the Beige Books more accurately represent the overall sentiment than others. We compare the measured sentiment with the macroeconomic time series. Our work highlights a potential application of LLMs for economic forecasting and is a novel approach to studying qualitative data critical to monetary policy in the United States.

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Sentiment Analysis with Large Language Models Applied to the Federal Reserve Beige Book

  • 会议论文

  • 发表日期：

    2025年

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  • Tom J. Espel

摘要

We present the application of Large Language Models (LLMs) to perform sentiment analysis on the United States Federal Reserve Beige Books. These reports are a critical qualitative resource for understanding the economic conditions in the United States and are instrumental in the decision-making of the Federal Reserve. Several past analyses have focused on the economic impact of the Beige Books. We present a novel approach by quantifying the report sentiment using recent sentiment analysis techniques. Our findings show that certain sections of the Beige Books more accurately represent the overall sentiment than others. We compare the measured sentiment with the macroeconomic time series. Our work highlights a potential application of LLMs for economic forecasting and is a novel approach to studying qualitative data critical to monetary policy in the United States.