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不同尺度下我们经济生活的复杂动态:来自搜索引擎查询数据的洞察。

Complex dynamics of our economic life on different scales: insights from search engine query data.

机构信息

Center for Polymer Studies, Department of Physics, 590 Commonwealth Avenue, Boston, MA 02215, USA.

出版信息

Philos Trans A Math Phys Eng Sci. 2010 Dec 28;368(1933):5707-19. doi: 10.1098/rsta.2010.0284.

Abstract

Search engine query data deliver insight into the behaviour of individuals who are the smallest possible scale of our economic life. Individuals are submitting several hundred million search engine queries around the world each day. We study weekly search volume data for various search terms from 2004 to 2010 that are offered by the search engine Google for scientific use, providing information about our economic life on an aggregated collective level. We ask the question whether there is a link between search volume data and financial market fluctuations on a weekly time scale. Both collective 'swarm intelligence' of Internet users and the group of financial market participants can be regarded as a complex system of many interacting subunits that react quickly to external changes. We find clear evidence that weekly transaction volumes of S&P 500 companies are correlated with weekly search volume of corresponding company names. Furthermore, we apply a recently introduced method for quantifying complex correlations in time series with which we find a clear tendency that search volume time series and transaction volume time series show recurring patterns.

摘要

搜索引擎查询数据深入洞察了个人行为,个人是我们经济生活中最小的单位。全世界每天有数十亿人在提交搜索引擎查询。我们研究了谷歌提供的 2004 年至 2010 年各种搜索词的每周搜索量数据,这些数据是为科学用途而提供的,汇总了经济生活的集体信息。我们想知道在每周时间尺度上,搜索量数据和金融市场波动之间是否存在联系。互联网用户的集体“群体智慧”和金融市场参与者群体都可以被视为一个由许多相互作用的子单元组成的复杂系统,这些子单元可以快速对外界变化做出反应。我们发现了明确的证据,表明标准普尔 500 家公司的每周交易量与相应公司名称的每周搜索量相关。此外,我们应用了一种最近引入的时间序列复杂相关性量化方法,通过该方法我们发现了一个明显的趋势,即搜索量时间序列和交易量时间序列显示出周期性模式。

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