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News and narratives in financial systems: Exploiting big data for systemic risk assessment
Journal of Economic Dynamics and Control ( IF 1.620 ) Pub Date : 2021-04-02 , DOI: 10.1016/j.jedc.2021.104119
Rickard Nyman , Sujit Kapadia , David Tuckett

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.



中文翻译:

金融系统中的新闻和叙述:利用大数据进行系统风险评估

本文将算法分析应用于基于金融市场文本的数据,以评估叙述和情绪如何驱动金融系统的发展。我们发现叙事中情感内容的变化在各个数据源之间具有高度相关性,并显示出全球金融危机之前繁荣的形成(以及随后的崩溃)。我们的指标对于其他常用的情绪指标也具有预测能力,并且似乎会影响经济变量。新颖的机器学习应用程序还指出,在危机发生之前,人们对围绕积极正面叙述的共识日益增多。总之,我们的指标可能有助于警告即将到来的金融系统困境。

更新日期:2021-04-23
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