英文标题：
《Quantifying macroeconomic expectations in stock markets using Google
Trends》
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作者：
Johannes Bock
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最新提交年份：
2018
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英文摘要：
Among other macroeconomic indicators, the monthly release of U.S. unemployment rate figures in the Employment Situation report by the U.S. Bureau of Labour Statistics gets a lot of media attention and strongly affects the stock markets. I investigate whether a profitable investment strategy can be constructed by predicting the likely changes in U.S. unemployment before the official news release using Google query volumes for related search terms. I find that massive new data sources of human interaction with the Internet not only improves U.S. unemployment rate predictability, but can also enhance market timing of trading strategies when considered jointly with macroeconomic data. My results illustrate the potential of combining extensive behavioural data sets with economic data to anticipate investor expectations and stock market moves.
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中文摘要：
在其他宏观经济指标中，美国劳工统计局（U.S.Bureau of Labour Statistics）在《就业形势报告》中每月公布的美国失业率数据引起了媒体的广泛关注，并对股市产生了重大影响。我调查是否可以通过在官方新闻发布前使用谷歌相关搜索词的查询量预测美国失业率的可能变化来构建一个有利可图的投资战略。我发现，人类与互联网互动的大量新数据源不仅提高了美国失业率的可预测性，而且在与宏观经济数据结合考虑时，还可以提高交易策略的市场时机。我的研究结果表明，将广泛的行为数据集与经济数据相结合，可以预测投资者的预期和股市走势。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Trading and Market Microstructure 交易与市场微观结构
分类描述：Market microstructure, liquidity, exchange and auction design, automated trading, agent-based modeling and market-making
市场微观结构，流动性，交易和拍卖设计，自动化交易，基于代理的建模和做市
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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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