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News and narratives in financial systems: Exploiting big data for systemic risk assessment

Rickard Nyman, Sujit Kapadia and David Tuckett

Journal of Economic Dynamics and Control, 2021, vol. 127, issue C

Abstract: This paper applies algorithmic analysis to financial market text-based data to assess how narratives and sentiment might drive financial system developments. We find changes in emotional content in narratives are highly correlated across data sources and show the formation (and subsequent collapse) of exuberance prior to the global financial crisis. Our metrics also have predictive power for other commonly used indicators of sentiment and appear to influence economic variables. A novel machine learning application also points towards increasing consensus around the strongly positive narrative prior to the crisis. Together, our metrics might help to warn about impending financial system distress.

Keywords: Systemic risk; Text mining; Big data; Sentiment; Uncertainty; Narratives; Early warning indicators (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:127:y:2021:i:c:s0165188921000543

DOI: 10.1016/j.jedc.2021.104119

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Journal of Economic Dynamics and Control is currently edited by J. Bullard, C. Chiarella, H. Dawid, C. H. Hommes, P. Klein and C. Otrok

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