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Analysis of CBDC narrative by central banks using large language models

Author

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  • Alonso-Robisco, Andres
  • Carbó, José Manuel

Abstract

One topic that is gaining importance in central bank communication is central bank digital currency (CBDC). To better understand central banks’ stance towards CBDCs, we used different natural language processing techniques on a set of central bank speeches. We found that the sentiment calculated by Large Language Models, and in particular by ChatGPT, is the one that most resembles the sentiment identified by human experts in those same speeches. Our study suggests that LLMs are an effective tool for improving sentiment measurements on specific policy texts, although they are not infallible and may be subject to new risks.

Suggested Citation

  • Alonso-Robisco, Andres & Carbó, José Manuel, 2023. "Analysis of CBDC narrative by central banks using large language models," Finance Research Letters, Elsevier, vol. 58(PC).
    • Handle: RePEc:eee:finlet:v:58:y:2023:i:pc:s1544612323010152
    DOI: 10.1016/j.frl.2023.104643
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    More about this item

    Keywords

    ChatGPT; BERT; CBDC; Digital money;
    All these keywords.

    JEL classification:

    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software

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