  We show how emotions extracted from macroeconomic news can be used to explain
and forecast future behaviour of sovereign bond yield spreads in Italy and
Spain. We use a big, open-source, database known as Global Database of Events,
Language and Tone to construct emotion indicators of bond market affective
states. We find that negative emotions extracted from news improve the
forecasting power of government yield spread models during distressed periods
even after controlling for the number of negative words present in the text. In
addition, stronger negative emotions, such as panic, reveal useful information
for predicting changes in spread at the short-term horizon, while milder
emotions, such as distress, are useful at longer time horizons. Emotions
generated by the Italian political turmoil propagate to the Spanish news
affecting this neighbourhood market.
