{
  "name": "Sentiment Analysis with Large Language Models Applied to the Federal Reserve Beige Book",
  "description": "We present the application of Large Language Models (LLMs) to perform sentiment analysis on the Beige Book of the United States Federal Reserve. 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. We use different LLM models over a dataset of more than ten years to evaluate their effectiveness in capturing sentiment in the reports. Our findings show that certain sections of the Beige Books are a more accurate representation of 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.",
  "image": "https://www.tomespel.com/p2.png",
  "external_url": "https://www.tomespel.com/p2/",
  "attributes": [
    {
      "trait_type": "Publication Date",
      "value": "2025-01-31"
    },
    {
      "trait_type": "Conference",
      "value": "IntelliSys 2025"
    },
    {
      "trait_type": "Series",
      "value": "Lecture Notes in Networks and Systems, vol 1554"
    },
    {
      "trait_type": "Publisher",
      "value": "Springer"
    },
    {
      "trait_type": "DOI",
      "value": "10.2139/ssrn.5120300"
    },
    {
      "trait_type": "Springer DOI",
      "value": "10.1007/978-3-031-99965-9_11"
    },
    {
      "trait_type": "SSRN ID",
      "value": "5120300"
    },
    {
      "trait_type": "Pages",
      "value": "16"
    },
    {
      "trait_type": "Dataset Period",
      "value": "2011-2023"
    },
    {
      "trait_type": "LLM Models Used",
      "value": "FinBERT, SiEBERT, DistilBERT, Twitter RoBERTa"
    }
  ],
  "properties": {
    "@context": "https://schema.org",
    "@type": "ScholarlyArticle",
    "author": [
      {
        "@type": "Person",
        "name": "Tom J. Espel",
        "affiliation": {
          "@type": "Organization",
          "name": "Independent"
        }
      }
    ],
    "datePublished": "2025-01-31",
    "dateModified": "2025-10-10",
    "keywords": [
      "large language model",
      "llm",
      "federal reserve",
      "sentiment analysis",
      "beige book"
    ],
    "isPartOf": {
      "@type": "PublicationVolume",
      "volumeNumber": "1554",
      "isPartOf": {
        "@type": "EventSeries",
        "name": "Intelligent Systems and Applications (IntelliSys 2025)",
        "publisher": {
          "@type": "Organization",
          "name": "Springer"
        }
      }
    },
    "numberOfPages": "16",
    "identifier": [
      {
        "@type": "PropertyValue",
        "propertyID": "DOI",
        "value": "10.2139/ssrn.5120300"
      },
      {
        "@type": "PropertyValue",
        "propertyID": "Springer DOI",
        "value": "10.1007/978-3-031-99965-9_11"
      },
      {
        "@type": "PropertyValue",
        "propertyID": "SSRN",
        "value": "5120300"
      }
    ],
    "url": [
      "https://www.tomespel.com/p2/",
      "https://ssrn.com/abstract=5120300"
    ],
    "citation": [
      {
        "@type": "ScholarlyArticle",
        "name": "Large language models (LLMs): survey, technical frameworks, and future challenges",
        "author": "P Kumar",
        "isPartOf": {
          "@type": "Periodical",
          "name": "Artificial Intelligence Review",
          "volumeNumber": "57"
        },
        "datePublished": "2024-08"
      },
      {
        "@type": "ScholarlyArticle",
        "name": "Foundations of Large Language Models",
        "author": "T Xiao, J Zhu",
        "datePublished": "2025-01"
      },
      {
        "@type": "Book",
        "name": "Natural language processing with transformers: building language applications with Hugging Face",
        "author": "L Tunstall, L V Werra, T Wolf",
        "publisher": {
          "@type": "Organization",
          "name": "O'Reilly Media"
        }
      },
      {
        "@type": "ScholarlyArticle",
        "name": "The Beige Book: Timely Information on the Regional Economy",
        "author": "D Ginther, M Zavdony",
        "isPartOf": {
          "@type": "Periodical",
          "name": "Atlanta Economic Review",
          "issueNumber": "3"
        },
        "datePublished": "2001"
      }
    ],
    "about": [
      {
        "@type": "Thing",
        "name": "Federal Reserve Beige Book",
        "description": "Analysis of 104 Fed Beige Books between 2011 and 2023"
      },
      {
        "@type": "Thing",
        "name": "LLM Sentiment Analysis",
        "description": "Comparison of FinBERT, SiEBERT, DistilBERT, and Twitter RoBERTa models"
      },
      {
        "@type": "Thing",
        "name": "Regional Fed Analysis",
        "description": "Richmond and Kansas City Fed branches found most representative of overall sentiment"
      },
      {
        "@type": "Thing",
        "name": "Economic Forecasting",
        "description": "Application of LLMs for monetary policy analysis"
      }
    ],
    "inLanguage": "en",
    "temporalCoverage": "2011/2023"
  }
}