Analysis of CBDC Narrative OF Central Banks using Large Language Models

Andres Alonso-Robisco and Jose Manuel Carbo
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Andres Alonso-Robisco: Banco de España
Jose Manuel Carbo: Banco de España

No 2321, Working Papers from Banco de España

Abstract: Central banks are increasingly using verbal communication for policymaking, focusing not only on traditional monetary policy, but also on a broad set of topics. One such topic is central bank digital currency (CBDC), which is attracting attention from the international community. The complex nature of this project means that it must be carefully designed to avoid unintended consequences, such as financial instability. We propose the use of different Natural Language Processing (NLP) techniques to better understand central banks’ stance towards CBDC, analyzing a set of central bank discourses from 2016 to 2022. We do this using traditional techniques, such as dictionary-based methods, and two large language models (LLMs), namely Bert and ChatGPT, concluding that LLMs better reflect the stance identified by human experts. In particular, we observe that ChatGPT exhibits a higher degree of alignment because it can capture subtler information than BERT. Our study suggests that LLMs are an effective tool to improve sentiment measurements for policy-specific texts, though they are not infallible and may be subject to new risks, like higher sensitivity to the length of texts, and prompt engineering.

Keywords: ChatGPT; BERT; CBDC; digital money (search for similar items in EconPapers)
JEL-codes: E58 G15 G41 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2023-08
New Economics Papers: this item is included in nep-ain, nep-ban, nep-big, nep-cba, nep-cmp, nep-mon and nep-pay
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:bde:wpaper:2321

DOI: 10.53479/33412

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