  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.
