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Working Paper
Identifying Financial Crises Using Machine Learning on Textual Data
We use machine learning techniques on textual data to identify financial crises. The onset of a crisis and its duration have implications for real economic activity, and as such can be valuable inputs into macroprudential, monetary, and fiscal policy. The academic literature and the policy realm rely mostly on expert judgment to determine crises, often with a lag. Consequently, crisis durations and the buildup phases of vulnerabilities are usually determined only with the benefit of hindsight. Although we can identify and forecast a portion of crises worldwide to various degrees with ...
Working Paper
Do Anecdotes Matter? Exploring the Beige Book through Textual Analysis from 1970 to 2025
We apply various natural language processing tools to see if the Beige Book is helpful in understanding economic activity. The Beige Book is a gathering of anecdotal compilations of current economic conditions from each Federal Reserve Bank, which is released to the public prior to FOMC meetings. We find that even controlling for lagged GDP growth and other metrics, the Beige Book sentiment provides meaningful explanatory power in nowcasting GDP growth and forecasting recessions, even more so than the yield spread or other news sentiment measures. The results on economic activity even hold in ...