Measures of beliefs, sentiment, and narratives often send recession signals that differ from those in hard data, defined as conventional economic and financial indicators. Using a real-time forecasting framework, we compare how soft and hard data predict recessions from one to twelve months ahead. Forecasts based on soft data are more responsive to rising recession risk: they identify more downturns, but also produce more false alarms. Even with far fewer inputs, soft-data forecasts remain competitive with hard-data forecasts out of sample, especially at shorter horizons. Combining hard and soft data often improves forecast performance, suggesting that the two types of information are useful complements.
Suggested citation:
Petrosky-Nadeau, Nicolas, Yeji Sung, and Daniel J. Wilson. 2026. “Do Vibes Predict Recessions? Evidence from a Big-Data Forecasting Framework.” Federal Reserve Bank of San Francisco Working Paper 2026-14. https://doi.org/10.24148/wp2026-14