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This paper applies algorithmic analysis to large amounts of financial market text-based data to assess how narratives and sentiment play a role in driving developments in the financial system. We find that changes in the emotional content in market narratives are highly correlated across data sources. They show clearly the formation (and subsequent collapse) of very high levels of sentiment — high excitement relative to anxiety — prior to the global financial crisis. Our metrics also have predictive power for other commonly used measures of sentiment and volatility and appear to influence economic and financial variables. And we develop a new methodology that attempts to capture the emergence of narrative topic consensus which gives an intuitive representation of increasing homogeneity of beliefs prior to the crisis. With increasing consensus around narratives high in excitement and lacking anxiety likely to be an important warning sign of impending financial system distress, the quantitative metrics we develop may complement other indicators and analysis in helping to gauge systemic risk.

News and Narratives in Financial Systems: Exploiting Big Data for Systemic Risk Assessment