Should Central Banks Care About Text Mining? A Literature Review

Jean-Charles Bricongne, Baptiste Meunier and Raquel Caldeira

Working papers from Banque de France

Abstract: As text mining has expanded in economics, central banks appear to also have ridden this wave, as we review use cases of text mining across central banks and supervisory institutions. Text mining is a polyvalent tool to gauge the economic outlook in which central banks operate, notably as an innovative way to measure inflation expectations. This is also a pivotal tool to assess risks to financial stability. Beyond financial markets, text mining can also help supervising individual financial institutions. As central banks increasingly consider issues such as the climate challenge, text mining also allows to assess the perception of climate-related risks and banks’ preparedness. Besides, the analysis of central banks’ communication provides a feedback tool on how to best convey decisions. Albeit powerful, text mining complements – rather than replaces – the usual indicators and procedures at central banks. Going forward, generative AI opens new frontiers for the use of textual data.

Keywords: Text Mining; Sentiment Analysis; Central Banking; Generative AI; Language Models (search for similar items in EconPapers)
JEL-codes: C38 C55 C82 E58 L82 (search for similar items in EconPapers)
Pages: 48 pages
Date: 2024
New Economics Papers: this item is included in nep-ain, nep-big, nep-cba and nep-mon
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Persistent link: https://EconPapers.repec.org/RePEc:bfr:banfra:950

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