英文标题：
《Big Data, Socio-Psychological Theory, Algorithmic Text Analysis and
Predicting the Michigan Consumer Sentiment Index》
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作者：
Rickard Nyman and Paul Ormerod
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最新提交年份：
2014
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英文摘要：
We describe an exercise of using Big Data to predict the Michigan Consumer Sentiment Index, a widely used indicator of the state of confidence in the US economy. We carry out the exercise from a pure ex ante perspective. We use the methodology of algorithmic text analysis of an archive of brokers\' reports over the period June 2010 through June 2013. The search is directed by the social-psychological theory of agent behaviour, namely conviction narrative theory. We compare one month ahead forecasts generated this way over a 15 month period with the forecasts reported for the consensus predictions of Wall Street economists. The former give much more accurate predictions, getting the direction of change correct on 12 of the 15 occasions compared to only 7 for the consensus predictions. We show that the approach retains significant predictive power even over a four month ahead horizon.
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中文摘要：
我们描述了一个使用大数据预测密歇根消费者信心指数（Michigan Consumer Intelligence Index）的练习，该指数是一个广泛使用的美国经济信心状态指标。我们纯粹是从事前的角度进行练习。我们使用算法文本分析方法，对2010年6月至2013年6月期间经纪人报告的档案进行分析。这一研究是以主体行为的社会心理学理论，即信念叙事理论为指导的。我们将以这种方式在15个月内生成的一个月前预测与华尔街经济学家一致预测的预测进行比较。前者给出了更准确的预测，在15次预测中，有12次得到了正确的变化方向，而共识预测只有7次。我们表明，即使在未来四个月内，该方法仍具有显著的预测能力。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Physics 物理学
二级分类：Physics and Society 物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
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