Title: Using the GDELT dataset to analyse the Italian sovereign bond market
Citation: LECTURE NOTES IN COMPUTER SCIENCE vol. 12565 p. 190-202
Publisher: Springer Verlag
Publication Year: 2020
JRC N°: JRC120224
ISSN: 1611-3349 (online),0302-9743 (print)
URI: https://link.springer.com/chapter/10.1007%2F978-3-030-64583-0_18
DOI: 10.1007/978-3-030-64583-0_18
Type: Articles in periodicals and books
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 in- formation of each news encoded in GDELT, we build indicators capturing investor's emotions which are useful to analyse the 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.
JRC Directorate:Joint Research Centre Corporate Activities

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