Using the GDELT Dataset to Analyse the Italian Sovereign Bond Market

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.

Data Science Technologies in Economics and Finance: A Gentle Walk-In

“…Data are broken into blocks and stored on different nodes, such that the DFS allows to work with partitioned data, that otherwise would become too big to be stored and analyzed on a single computer. Frameworks that heavily use DFS include Apache Hadoop 14 and Amazon S3, 15 the backbone of storage on AWS. There are a variety of platforms for wrangling and analyzing distributed data, the most prominent of which perhaps is Apache Spark.…”

“…In economics, NLP tools have been adapted and further developed for extracting relevant concepts, sentiments, and emotions from social media and news (see, e.g., [37,24,14,4], among others). These technologies applied in the economic context facilitate data integration from multiple heterogeneous sources, enable the development of information filtering systems, and support knowledge discovery tasks.…”

GPT4MTS: Prompt-based Large Language Model for Multimodal Time-series Forecasting

“…GDELT database records global events and their associated media coverage, underpinning the profound impact that news can have in guiding our lives. It propels the development of computational communication research (Hopp et al 2019;Lock 2020) under multiple domains, including social unrest (Galla and Burke 2018), government policies (Schintler and Kulkarni 2014), and financial market (Consoli, Pezzoli, and Tosetti 2020). The establishment of our dataset can enhance the accessibility of different fields to multimodal time series datasets, as well as foster further research in multimodal computational communication analysis.…”