Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/340165 
Year of Publication: 
2026
Series/Report no.: 
Bank of Finland Research Discussion Papers No. 3/2026
Publisher: 
Bank of Finland, Helsinki
Abstract: 
We develop a novel sentiment measure from survey forecasts that captures the component of beliefs arising from the systematic misaggregation of public information relative to a machine benchmark based on the same information set. We extend this sentiment measure historically for a panel of 78 countries using machine learning models trained on BERT embeddings of historical news articles (1903-2020). The backcasted sentiment shows that shocks in median sentiment predict credit booms in the non-tradable corporate sector, which prior research has linked to financial crises. We further find that this sentiment component is shaped by memory-related dynamics, as the time elapsed since major crises and the share of young-to-old people in the population predict surges in optimism even when recent economic developments are controlled for. Taken together, the findings provide new historical evidence consistent with the Minsky-Kindleberger view on financial crises.
Subjects: 
Survey data
Sentiment
Memory
Machine Learning
Text Data
Credit growth
Financial Crisis
JEL: 
E44
E51
G01
D84
G41
E32
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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