Sentiment analysis of the Spanish financial stability Report

Ángel Iván Moreno Bernal and Carlos González Pedraz

International Review of Economics & Finance, 2024, vol. 89, issue PB, 913-939

Abstract: This paper presents a text mining application, to extract information from financial texts and use this information to create sentiment indices. In particular, the analysis focuses on the Banco de España's Financial Stability Reports from 2002 to 2019 in their Spanish version and on the press reaction to these reports. To calculate the indices, a Spanish dictionary of words with a positive, negative or neutral connotation has been created, to the best of our knowledge the first within the context of financial stability. The robustness of the indices is analysed by applying them to different sections of the Report, and using different variations of the dictionary and the definition of the index. Finally, sentiment is also measured for press reports in the days following the publication of the Report. The results show that the list of words collected in the reference dictionary represents a robust sample to estimate the sentiment of these texts. This tool constitutes a valuable methodology to analyse the repercussion of financial stability reports, while objectively quantifying the sentiment conveyed in them.

Keywords: Text mining; Sentiment analysis; Natural language processing; Central bank communications; Financial stability (search for similar items in EconPapers)
JEL-codes: C82 G28 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reveco:v:89:y:2024:i:pb:p:913-939

DOI: 10.1016/j.iref.2023.10.037

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