New Online Investor Sentiment and Asset Returns

This paper proposes two data-driven econometric approaches to construct online investor sentiment indices based on internet search queries, which are built by the partial least squares and LASSO methods, respectively. By examining the relationship between investor sentiment and stock risk premium on overall market level, our empirical findings are that these sentiment indices have predictive power both in and out of sample, and the out-of-sample predictability of the online investor sentiment indices proposed by the paper is robust for different horizons. Moreover, our investor sentiment indices are also able to predict the returns of cross-sectional characteristics portfolios. This predictability based on investor sentiment has economic value since it improves portfolio performance, in terms of certainty equivalent return gain and Sharpe ratio, for investors who conduct the optimal asset allocation.