英文标题：
《Do Google Trend data contain more predictability than price returns?》
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作者：
Damien Challet and Ahmed Bel Hadj Ayed
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最新提交年份：
2014
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英文摘要：
Using non-linear machine learning methods and a proper backtest procedure, we critically examine the claim that Google Trends can predict future price returns. We first review the many potential biases that may influence backtests with this kind of data positively, the choice of keywords being by far the greatest culprit. We then argue that the real question is whether such data contain more predictability than price returns themselves: our backtest yields a performance of about 17bps per week which only weakly depends on the kind of data on which predictors are based, i.e. either past price returns or Google Trends data, or both.
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中文摘要：
使用非线性机器学习方法和适当的回溯测试程序，我们批判性地检验了谷歌趋势可以预测未来价格回报的说法。我们首先回顾了许多可能会对此类数据的回溯测试产生积极影响的潜在偏差，到目前为止，关键词的选择是最大的罪魁祸首。然后，我们认为真正的问题是，这些数据是否比价格回报本身更具可预测性：我们的回溯测试每周产生约17个基点的表现，这仅弱地取决于预测因素所基于的数据类型，即过去的价格回报或谷歌趋势数据，或两者兼而有之。
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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：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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