Emotions in Macroeconomic News and their Impact on the European Bond Market

TL;DR

This paper demonstrates that emotions derived from macroeconomic news can enhance the prediction of sovereign bond yield spreads in Italy and Spain, especially during distressed periods, by capturing market sentiment and emotional states.

Contribution

It introduces a novel approach using emotion indicators from a large open-source database to improve bond spread forecasting and analyzes cross-country emotional influence in European markets.

Findings

Negative emotions improve forecast accuracy during distress

Stronger emotions like panic predict short-term spread changes

Milder emotions like distress are useful for long-term predictions

Abstract

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.