英文标题：
《Measuring Financial Sentiment to Predict Financial Instability: A New
Approach based on Text Analysis》
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作者：
Paul Ormerod, Rickard Nyman, David Tuckett
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最新提交年份：
2015
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英文摘要：
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.
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中文摘要：
在21世纪末的金融危机之后，政策制定者对监控金融稳定表现出了极大的兴趣。几家中央银行现在发布了金融压力指数，这些指数基本上基于市场相关数据。在本文中，我们通过推导经济中情绪变化的信息来检验改善指数的潜力。我们报告了一种基于超大文本数据库内容分析的新方法，称为定向算法文本分析。该算法非常迅速地识别出两个核心情感群体之间的关系随着时间的推移而发生的变化。该方法具有鲁棒性。在不同的研究中，相同的词表用于识别两个情绪组。列表中单词的成员身份已经在心理学实验中得到验证。这些单词由日常英语单词组成，没有特定的经济意义。初步结果显示了希望。情绪指数捕捉了文本中两个情绪组之间的变化，可能指的是整个美国经济，改善了对实际GDP增长的四分之一提前共识预测。更具体地说，克利夫兰和圣路易斯联邦储备银行的金融压力指数都显示了相同的格兰杰因果指数。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：General Finance 一般财务
分类描述：Development of general quantitative methodologies with applications in finance
通用定量方法的发展及其在金融中的应用
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