News and narratives in financial systems: Exploiting big data for systemic risk assessment
本文用算法分析金融市场文本数据，发现叙事中的情绪变化与数据源高度相关，并在全球金融危机前形成并崩溃了过度乐观情绪，这些指标能预测其他情绪指标并影响经济变量，有助于预警金融系统困境。
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.