Working Paper: NBER ID: w29344
Authors: Leland Bybee; Bryan T. Kelly; Asaf Manela; Dacheng Xiu
Abstract: 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 1984–2017, 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 explains 25% of aggregate stock market returns. A text-augmented VAR demonstrates the large incremental role of news text in modeling macroeconomic dynamics. We use this model to retrieve the narratives that underlie business cycle fluctuations.
Keywords: business news; business cycles; textual analysis; macroeconomic dynamics; topic modeling
JEL Codes: E32; G0
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| news attention (E60) | aggregate stock market fluctuations (E30) |
| news attention (recession topic) (F44) | future output (E23) |
| news attention (recession topic) (F44) | future employment (J68) |
| news attention (E60) | LBO activity (G34) |
| news attention (IPO-related topics) (G24) | IPO volume (G24) |