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
Stock-induced Google trends and the predictability of sectoral stock returns