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.

我们展示了如何使用从宏观经济新闻中提取的情绪来解释和预测意大利和西班牙主权债券收益率利差的未来行为。我们使用一个名为全球事件、语言和语气数据库的大型开源数据库来构建债券市场情感状态的情感指标。我们发现，即使在控制了文本中出现的负面词的数量之后，从新闻中提取的负面情绪也提高了政府收益率传播模型在困境时期的预测能力。此外，更强烈的负面情绪，如恐慌，揭示了预测短期传播变化的有用信息，而温和的情绪，如痛苦，在更长的时间范围内是有用的。