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This study explores the possibilities of applying Google Trends to exchange rate forecasting. Specifically, we construct a sentiment index by using Google Trends to capture market sentiment in Japan and the United States. We forecast the USD/JPY rates using three structural models and two autoregressive models and examine whether our sentiment index can improve the predictive power of these models. We also check the robustness of the main results using the Taylor rule-based model and rolling regression methodology. The data we use run from January 2004 to August 2018, treating January 2004 to February 2011 as the training sample and March 2011 to August 2018 as the forecast sample. We find that the addition of the sentiment index into these models decreases the mean squared prediction error. We also test the sentiment indices of different word numbers and find that the 25-and 30-word indices perform best; in particular, the 30-word index improves all the models tested in this study.

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