Quantifying Minsky cycles

Kim Ristolainen

No 3/2026, Bank of Finland Research Discussion Papers from Bank of Finland

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.

Keywords: Survey data; Sentiment; Memory; Machine Learning; Text Data; Credit growth; Financial Crisis (search for similar items in EconPapers)
JEL-codes: D84 E32 E44 E51 G01 G41 (search for similar items in EconPapers)
Date: 2026
New Economics Papers: this item is included in nep-his and nep-pke
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https://www.econstor.eu/bitstream/10419/340165/1/1968822712.pdf (application/pdf)

Related works:
Working Paper: Quantifying Minsky Cycles (2026)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:bofrdp:340165

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