TY - GEN
T1 - Using the GDELT Dataset to Analyse the Italian Sovereign Bond Market
AU - Consoli, Sergio
AU - Pezzoli, Luca Tiozzo
AU - Tiozzo Pezzoli, Luca
AU - Tosetti, Elisa
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Big data management
KW - Feature Engineering
KW - GDELT
KW - Government yield spread
KW - Machine learning
KW - Quantile regression
KW - Big data management
KW - Feature Engineering
KW - GDELT
KW - Government yield spread
KW - Machine learning
KW - Quantile regression
UR - http://hdl.handle.net/10807/179501
U2 - 10.1007/978-3-030-64583-0_18
DO - 10.1007/978-3-030-64583-0_18
M3 - Conference contribution
SN - 978-3-030-64582-3
VL - 12565
T3 - LECTURE NOTES IN COMPUTER SCIENCE
SP - 190
EP - 202
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
T2 - 6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020
Y2 - 19 July 2020 through 23 July 2020
ER -