We propose an approach to measuring the state of the economy via textual analysis of business news. From the full text of 800,000 Wall Street Journal articles for 1984 to 2017, we estimate a topic model that summarizes business news into interpretable topical themes and quantifies the proportion of news attention allocated to each theme over time. News attention closely tracks a wide range of economic activities and can forecast aggregate stock market returns. A text-augmented vector autoregression demonstrates the large incremental role of news text in forecasting macroeconomic dynamics. We retrieve the narratives that underlie these improvements in market and business cycle forecasts.

中文翻译：

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商业新闻和经济周期


我们提出了一种通过商业新闻的文本分析来衡量经济状况的方法。根据 1984 年至 2017 年 80 万篇《华尔街日报》文章的全文，我们估计了一个主题模型，该模型将商业新闻总结为可解释的主题，并量化随着时间的推移分配给每个主题的新闻注意力比例。新闻关注密切跟踪广泛的经济活动，并可以预测股市总回报。文本增强向量自回归证明了新闻文本在预测宏观经济动态中的巨大增量作用。我们检索了市场和商业周期预测这些改进背后的叙述。