Forecasting Abnormal Stock Returns and Trading Volume Using Investor Sentiment: Evidence from Online Search ?

We examine the ability of online ticker searches (e.g. XOM for Exxon Mobil) to forecast abnormal stock returns and trading volumes. Specifically, we argue that online ticker search serves as a valid proxy for investor sentiment – a set of beliefs about cash flows and investments risks that are not necessarily justified by the facts at hand – which is generally associated with less sophisticated, retail investors. Based on prior research on investor sentiment, we expect online search intensity to forecast stock returns and trading volume, and that highly volatile stocks, which are more difficult to arbitrage, will be more sensitive to search intensity than less volatile stocks. In a sample of S&P 500 firms over the period 2005–2008, we find that, over a weekly horizon, online search intensity reliably predicts abnormal stock returns and trading volume, and that the sensitivity of returns to search intensity is positively related to the difficulty with which a stock can be arbitraged. We conclude by offering guidelines for the utilization of online search data in other forecasting