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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Can central bank speeches predict financial market turbulence? Evidence from an adaptive NLP sentiment index analysis using XGBoost machine learning technique
Original language description
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'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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science 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)

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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