标题： 衡量金融情绪以预测金融不稳定：一种基于文本分析的新方法 显示英文标题

标题： Measuring Financial Sentiment to Predict Financial Instability: A New Approach based on Text Analysis

摘要： 在2000年代末的金融危机之后，政策制定者表现出对监测金融稳定性的浓厚兴趣。几家中央银行现在发布金融压力指数，这些指数基本上基于市场相关数据。在本文中，我们研究了通过获取经济情绪变化的信息来改进这些指数的潜力。我们报告了一种新方法，该方法基于对非常大的文本数据库的内容分析，称为定向算法文本分析。该算法能够快速识别两个核心情感组之间关系随时间的变化。该方法具有稳健性。同一词列表用于在不同研究中识别两种情感组。词列表中的词语已在心理学实验中得到验证。这些词语由日常英语单词组成，没有特定的经济含义。初步结果显示出希望。一个捕捉到文本中两个情感组之间变化的情绪指数，有可能涉及整个美国经济，可以改善对未来一个季度的名义GDP增长共识预测。更具体地说，同样的指数被证明是克利夫兰和圣路易斯联邦储备银行金融压力指数的格兰杰原因。 显示英文摘要

摘要： Following the financial crisis of the late 2000s, policy makers have shown considerable interest in monitoring financial stability. Several central banks now publish indices of financial stress, which are essentially based upon market related data. In this paper, we examine the potential for improving the indices by deriving information about emotion shifts in the economy. We report on a new approach, based on the content analysis of very large text databases, and termed directed algorithmic text analysis. The algorithm identifies, very rapidly, shifts through time in the relations between two core emotional groups. The method is robust. The same word-list is used to identify the two emotion groups across different studies. Membership of the words in the lists has been validated in psychological experiments. The words consist of everyday English words with no specific economic meaning. Initial results show promise. An emotion index capturing shifts between the two emotion groups in texts potentially referring to the whole US economy improves the one-quarter ahead consensus forecasts for real GDP growth. More specifically, the same indices are shown to Granger cause both the Cleveland and St Louis Federal Reserve Indices of Financial Stress.