This study proposes a new method of incorporating emotions from newspaper articles into macroeconomic forecasts, attempting to forecast industrial production and consumer prices leveraging narrative and sentiment from global newspapers. For the most part, existing research includes positive and negative tone only to improve macroeconomic forecasts, focusing predominantly on large economies such as the US. These works use mainly anglophone sources of narrative, thus not capturing the entire complexity of the multitude of emotions contained in global news articles. This study expands the existing body of research by incorporating a wide array of emotions from newspapers around the world – extracted from the Global Database of Events, Language and Tone (GDELT) – into macroeconomic forecasts. We present a thematic data filtering methodology based on a bi-directional long short term memory neural network (Bi-LSTM) for extracting emotion scores from GDELT and demonstrate its effectiveness by comparing results for filtered and unfiltered data. We model industrial production and consumer prices across a diverse range of economies using an autoregressive framework, and find that including emotions from global newspapers significantly improves forecasts compared to three autoregressive benchmark models. We complement our forecasts with an interpretability analysis on distinct groups of emotions and find that emotions associated with happiness and anger have the strongest predictive power for the variables we predict.

这项研究提出了一种将报纸文章的情绪纳入宏观经济预测的新方法，试图利用全球报纸的叙述和情绪来预测工业生产和消费者价格。大多数情况下，现有研究包括正面和负面基调，只是为了改善宏观经济预测，主要侧重于美国等大型经济体。这些作品主要使用英语的叙事来源，因此无法捕捉全球新闻文章中所包含的多种情感的全部复杂性。这项研究通过将全球报纸（从事件，语言和语气全球数据库（GDELT）中摘录）中的各种情绪纳入宏观经济预测中，扩大了现有的研究范围。我们提出了一种基于双向长期短期记忆神经网络（Bi-LSTM）的主题数据过滤方法，用于从GDELT中提取情绪得分，并通过比较过滤数据和未过滤数据的结果证明了其有效性。我们使用自回归框架对各种经济体的工业生产和消费者价格进行建模，发现与三种自回归基准模型相比，包含全球报纸的情绪可以显着改善预测。我们通过对不同情绪类别的可解释性分析来补充我们的预测，发现与幸福和愤怒相关的情绪对我们预测的变量具有最强的预测能力。