Unveiling the sentiment behind central bank narratives: A novel deep learning index

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Unveiling the sentiment behind central bank narratives: A novel deep learning index

Popis výsledku v původním jazyce

"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."

Název v anglickém jazyce

Unveiling the sentiment behind central bank narratives: A novel deep learning index

Popis výsledku anglicky

"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."

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

"Journal of Behavioral and Experimental Finance"

ISSN

2214-6350

e-ISSN

—

Svazek periodika

38

Číslo periodika v rámci svazku

2023

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

1-15

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85153590473