Unveiling the sentiment behind central bank narratives: A novel deep learning index
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3ADIW4GPHP" target="_blank" >RIV/00216208:11320/23:DIW4GPHP - isvavai.cz</a>
Result on the web
<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153590473&doi=10.1016%2fj.jbef.2023.100809&partnerID=40&md5=2ed9eb622bb72cddb6fb2b947504d0bb" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153590473&doi=10.1016%2fj.jbef.2023.100809&partnerID=40&md5=2ed9eb622bb72cddb6fb2b947504d0bb</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.jbef.2023.100809" target="_blank" >10.1016/j.jbef.2023.100809</a>
Alternative languages
Result language
angličtina
Original language name
Unveiling the sentiment behind central bank narratives: A novel deep learning index
Original language description
"This paper proposes a new framework for analyzing the sentiments of central bank narratives. Specifically, we fine-tune a pre-trained BERT model on a dataset of manually annotated sentences on monetary policy stance. We derive a deep learning domain-specific model—BERT central bank sentiment index—ready for sentiment predictions. The proposed index performs similarly to other measures in capturing financial uncertainty. Also, the sentiment index is less noisy and has the ability to forecast the future path of policy stance, augmenting the standard Taylor rule. Finally, compared to other lexicon-based sentiment indicators, our deep learning index has a higher predictive power in anticipating policy rates changes. Our framework enables future possible research in developing more accurate sentiment indicators for central banks in both advanced and emerging countries. © 2023 Elsevier B.V."
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
"Journal of Behavioral and Experimental Finance"
ISSN
2214-6350
e-ISSN
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Volume of the periodical
38
Issue of the periodical within the volume
2023
Country of publishing house
US - UNITED STATES
Number of pages
15
Pages from-to
1-15
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-85153590473