Quantifying Minsky Cycles

We develop a novel sentiment measure derived from survey data to empirically vali date the Minsky–Kindleberger view on financial crises. Using survey data from multiple countries, we decompose beliefs into components explained by public information that are orthogonal to optimal machine beliefs, constructing a framework that isolates sentiment and its dispersion among individuals. We show that deviations from machine-optimized benchmarks arise from systematic misaggregation of public information. The sentiment measure is validated through its predictive relationships with financial markets and belief dynamics consistent with heterogeneous-beliefs asset pricing theory. We extend this senti ment 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, providing the first historically large-scale empirical validation of the Minsky cycle. We further show that sentiment, which is a misaggregation of public information, is influenced by memory-related dynamics, as the time elapsed since major crises and the share of young-to-old people in the population strongly predict surges in optimism even when recent economic developments are controlled for.