TY - GEN
T1 - Information Extraction From the GDELT Database to Analyse EU Sovereign Bond Markets
AU - Tiozzo Pezzoli, Luca
AU - Tosetti, Elisa
AU - Consoli, Sergio
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Big data
KW - Features engineering
KW - GDELT
KW - Government yield spread
KW - Machine learning
KW - Big data
KW - Features engineering
KW - GDELT
KW - Government yield spread
KW - Machine learning
UR - http://hdl.handle.net/10807/179506
U2 - 10.1007/978-3-030-66981-2_5
DO - 10.1007/978-3-030-66981-2_5
M3 - Conference contribution
SN - 978-3-030-66980-5
VL - 12591
T3 - LECTURE NOTES IN COMPUTER SCIENCE
SP - 55
EP - 67
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
T2 - 5th Workshop on Mining Data for Financial Applications, MIDAS 2020, held in conjunction with ECML PKDD 2020
Y2 - 14 September 2020 through 18 September 2020
ER -