Forecasting inflation using sentiment

Patrick Eugster and Matthias W. Uhl

Economics Letters, 2024, vol. 236, issue C

Abstract: Using algorithmically scored sentiment of almost 730.000 news articles between Q1 2003 and Q4 2021, we construct an index and analyze its predictive power for US inflation for up to eight quarters. In a pseudo out-of-sample setting, we show that sentiment is able to forecast inflation more accurately than a naïve random walk with root mean squared errors that are around 30 percent lower depending on the forecasting horizon. Against other often used benchmarks, forecasting models using macroeconomic variables and Michigan surveys, forecasting accuracy of our sentiment index tends to outperform for shorter forecasting horizons.

Keywords: Behavioral finance; Inflation forecast; News sentiment; NLP (search for similar items in EconPapers)
JEL-codes: E31 G40 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:236:y:2024:i:c:s0165176524000582

DOI: 10.1016/j.econlet.2024.111575

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