Information Extraction From the GDELT Database to Analyse EU Sovereign Bond Markets

Luca Tiozzo Pezzoli, Elisa Tosetti, Sergio Consoli

Risultato della ricerca: Contributo in libroContributo a convegno

Abstract

In this contribution we provide an overview of a currently on-going project related to the development of a methodology for building economic and financial indicators capturing investor’s emotions and topics popularity which are useful to analyse the sovereign bond markets of countries in the EU.These alternative indicators are obtained from the Global Data on Events, Location, and Tone (GDELT) database, which is a real-time, open-source, large-scale repository of global human society for open research which monitors worlds broadcast, print, and web news, creating a free open platform for computing on the entire world’s media. After providing an overview of the method under development, some preliminary findings related to the use case of Italy are also given. The use case reveals initial good performance of our methodology for the forecasting of the Italian sovereign bond market using the information extracted from GDELT and a deep Long Short-Term Memory Network opportunely trained and validated with a rolling window approach to best accounting for non-linearities in the data.
Lingua originaleEnglish
Titolo della pubblicazione ospiteLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pagine55-67
Numero di pagine13
Volume12591
DOI
Stato di pubblicazionePubblicato - 2021
Pubblicato esternamenteYes
Evento5th Workshop on Mining Data for Financial Applications, MIDAS 2020, held in conjunction with ECML PKDD 2020 - bel
Durata: 14 set 202018 set 2020

Serie di pubblicazioni

NomeLECTURE NOTES IN COMPUTER SCIENCE

Convegno

Convegno5th Workshop on Mining Data for Financial Applications, MIDAS 2020, held in conjunction with ECML PKDD 2020
Cittàbel
Periodo14/9/2018/9/20

Keywords

  • Big data
  • Features engineering
  • GDELT
  • Government yield spread
  • Machine learning

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