Abstract
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.
Highlights
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
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
In this contribution we have presented our work-in-progress related to the development of a methodology for building alternative economic and financial indicators capturing investor’s emotions and topics popularity from GDELT, the Global Data on Events, Location, and Tone database, a free open platform containing real-time worlds broadcast, print, and web news
Summary
Economic and fiscal policies conceived by international organizations, governments, and central banks heavily depend on economic forecasts, in particular during times of economic turmoil like the one we have recently experienced with the COVID-19 virus spreading world-wide [30]. In this context, the use of recent Big Data technologies for improving forecasting and nowcasting for several types of economic and financial applications has high potentials. In a currently on-going project we are designing a methodology to extract alternative economic and financial indicators capturing investor’s emotions, topics popularity, and economic and political events, from the Global Database of Events, Language and Tone (GDELT)1 [17], a novel big database of news information. The news-based economic and financial indicators extracted from GDELT can be used as alternative features to enrich forecasting and nowcasting models for the analysis of the sovereign bond markets of countries in the EU. The selected variables capture, among others, investor’s emotions, economic and political events, and popularity of news thematics for that country These additional variables are included into economic forecasting and nowcasting models with the goal of improving their performance. In current research we are experimenting different models, ranging from traditional economic models to novel machine learning approaches, like Gradient Boosting Machines and Recurrent Neural Networks (RNNs), which have been shown to be successful in various forecasting problems in Economics and Finance (see e.g. [4,6–8,16,18,29] among others)
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