Neural forecasting of the Italian sovereign bond market with economic news

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.