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.
Highlights
The turbulence in government yield spreads observed since the intensification of the financial crisis in 2009 in countries within the Euro area has originated an intense debate about the drivers of the sovereign bond market
For each news Global Knowledge Graph (GKG) extracts information on people, locations and organizations mentioned in the news, it retrieves counts, quotes, images and themes present in the news using a number of popular topical taxonomies, such as the World Bank Topical Taxonomy (WB),10 or the GDELT built-in topical taxonomy
In this paper we studied the effect of news emotions to help predicting changes in government yield bond spread in Italy and Spain
Summary
The turbulence in government yield spreads observed since the intensification of the financial crisis in 2009 in countries within the Euro area has originated an intense debate about the drivers of the sovereign bond market. An emerging literature having its roots in behavioural finance points at human perception, instinct and sentiment of investors as important elements that may guide their judgement and decision making, impacting their investment decisions (Blommestein et al, 2012) To account for these factors, recent studies propose to measure sentiment from pieces of text such as news, blogs and other forms of written communication and use it to model and predict developments in the financial market (Tetlock, 2007; Garcia, 2013). In this paper we argue that affective states such as distress, or fear, extracted from macroeconomic news may better capture, relative to simple univariate sentiment indicators used in previous studies, elements linked to human perception and mood that influence the behaviour and decision making of investors.
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