Series: Working Papers. 2011.
Author: Ángel Iván Moreno Bernal and Carlos González Pedraz.
Published in: International Review of Economics and Finance, Volume 89, Part B, January 2024, Pages 913-939
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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.