In this paper, we consider Google trends as a measure of investors? attention in the predictability of stock returns across eleven major US sectors The theoretical motivation for our paper is clear In seeking information to guide investment decisions, investors? sentiments are shaped by news that could induce changes in the prices of stocks which Google trends data have been argued more fitting We make theoretical, empirical and methodological contributions in this area We account for evident asymmetry in Google trends (G-trends hereafter) to explain positive and negative worded news in the predictability of stock returns Methodologically and empirically, we compare single- and multi-factor predictive models augmented with distinctive statistical effects against the baseline time series model to forecast sectoral stocks for the US We highlight three key findings One, G-trends record consistent negative correlations with stock returns across sectors Two, the proposed predictive model with G-trends outperforms the baseline (random walk) model Three, the inclusion of asymmetry and macroeconomic variables improves the out-performance of G-trends over the baseline model

中文翻译：

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股票引发的谷歌趋势和部门股票回报的可预测性

在本文中，我们将 Google 趋势视为衡量投资者的指标？关注美国 11 个主要行业股票回报的可预测性 我们论文的理论动机很明确 在寻找指导投资决策的信息时，投资者？情绪是由可能导致股票价格变化的新闻形成的，谷歌趋势数据被认为更合适我们在这一领域做出理论、经验和方法论贡献我们解释了谷歌趋势（以下简称 G 趋势）的明显不对称性，以解释股票回报可预测性中的正面和负面新闻从方法论和经验上讲，