Can central bank speeches predict financial market turbulence? Evidence from an adaptive NLP sentiment index analysis using XGBoost machine learning technique

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10441759" target="_blank" >RIV/00216208:11320/21:10441759 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=-HRnpoZ4br" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=-HRnpoZ4br</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cbrev.2021.12.002" target="_blank" >10.1016/j.cbrev.2021.12.002</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Can central bank speeches predict financial market turbulence? Evidence from an adaptive NLP sentiment index analysis using XGBoost machine learning technique

Popis výsledku v původním jazyce

Central Bank speeches usually function as aggregators of internal quantitative and qualitative analysis of the institutions regarding the macro economy, the monetary policy and the health of the financial systems. Speeches usually function as a summary of the current status of a countries economic health, the undergoing trends and some future perspectives of the global economy. In this study departing from classical econometrics we employ natural language processing technologies in combination with machine learning techniques in order to filter out the most important signals in the corpus of speeches and translate into a sentiment index for forecasting the future financial markets behaviour. In our analysis, it is evident that central banker&apos;s expectations on economy tend to exhibit a predictive ability for financial markets turmoil. Using a combination of dictionaries which are either predefined or build based on historical speeches of the corpus we train an Extreme Gradient Boosting model that generates a sentiment index which signals turmoil with acceptable accuracy when passing a specific threshold.

Název v anglickém jazyce

Can central bank speeches predict financial market turbulence? Evidence from an adaptive NLP sentiment index analysis using XGBoost machine learning technique

Popis výsledku anglicky

Central Bank speeches usually function as aggregators of internal quantitative and qualitative analysis of the institutions regarding the macro economy, the monetary policy and the health of the financial systems. Speeches usually function as a summary of the current status of a countries economic health, the undergoing trends and some future perspectives of the global economy. In this study departing from classical econometrics we employ natural language processing technologies in combination with machine learning techniques in order to filter out the most important signals in the corpus of speeches and translate into a sentiment index for forecasting the future financial markets behaviour. In our analysis, it is evident that central banker&apos;s expectations on economy tend to exhibit a predictive ability for financial markets turmoil. Using a combination of dictionaries which are either predefined or build based on historical speeches of the corpus we train an Extreme Gradient Boosting model that generates a sentiment index which signals turmoil with acceptable accuracy when passing a specific threshold.

Druh

J_imp - Článek v periodiku v databázi Web of Science

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

2021

Kód důvěrnosti údajů

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

Název periodika

Central Bank Review

ISSN

1303-0701

e-ISSN

1305-8800

Svazek periodika

21

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

TR - Turecká republika

Počet stran výsledku

13

Strana od-do

141-153

Kód UT WoS článku

000756700800003

EID výsledku v databázi Scopus

2-s2.0-85121267850