Neural Forecasting Of The Italian Sovereign Bond Market With Economic News

This paper, by researchers at the European Commission's Joint Research Centre and Department of Management, Università Ca’ Foscari Venezia uses GDELT to forecast bond market interest rate spread.

In this paper, we employ economic news within a neural network framework to forecast the Italian 10-year interest rate spread. We use a big, open-source, database known as Global Database of Events, Language and Tone to extract topical and emotional news content linked to bond markets dynamics. We deploy such information within a probabilistic forecasting framework with autoregressive recurrent networks (DeepAR). Our findings suggest that a deep learning network based on long short-term memory cells outperforms classical machine learning techniques and provides a forecasting performance that is over and above that obtained by using conventional determinants of interest rates alone.

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