Using the GDELT Dataset to Analyse the Italian Sovereign Bond Market

Luca Tiozzo Pezzoli, Elisa Tosetti, Sergio Consoli, Luca Tiozzo Pezzoli

Risultato della ricerca: Contributo in libroContributo a convegno

Abstract

The Global Data on Events, Location, and Tone (GDELT) is a real time large scale database 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. In this work, we first describe a data crawler, which collects metadata of the GDELT database in real-time and stores them in a big data management system based on Elasticsearch, a popular and efficient search engine relying on the Lucene library. Then, by exploiting and engineering the detailed information of each news encoded in GDELT, we build indicators capturing investor’s emotions which are useful to analyse the sovereign bond market in Italy. By using regression analysis and by exploiting the power of Gradient Boosting models from machine learning, we find that the features extracted from GDELT improve the forecast of country government yield spread, relative that of a baseline regression where only conventional regressors are included. The improvement in the fitting is particularly relevant during the period government crisis in May-December 2018.
Lingua originaleEnglish
Titolo della pubblicazione ospiteLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pagine190-202
Numero di pagine13
Volume12565
DOI
Stato di pubblicazionePubblicato - 2020
Pubblicato esternamenteYes
Evento6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020 - ita
Durata: 19 lug 202023 lug 2020

Serie di pubblicazioni

NomeLECTURE NOTES IN COMPUTER SCIENCE

Convegno

Convegno6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020
Cittàita
Periodo19/7/2023/7/20

Keywords

  • Big data management
  • Feature Engineering
  • GDELT
  • Government yield spread
  • Machine learning
  • Quantile regression

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