The authors analyze Google Search Volume Index (GSVI) data for search frequencies related to four search terms: “market crash,” “market rally,” “bear market,” and “bull market.” They hypothesize that changes in relative search volume proxy for immediate market sentiment because they represent where average investors might be focusing their attention. They predict that negative near-term returns will follow an increase in the volume of negative search terms (crash and bear) and that positive returns will follow an increase in the volume of positive search terms (rally and bull). Their findings indicate predictive information in three terms (crash, bear, and rally) but no significant information in one term (bull).

If search term volumes contain reliable indicators of current investor sentiment, then traders could monitor such volumes and perhaps gain an edge over traditional fundamental or technical analysis when timing a trade. A persistent indicator would also find its way into the academic toolbox, and we should expect that such results would drive other studies that explore additional search terms.

If Google controls the search term volume data, it could sell this information to the highest bidder, possibly making it cost-prohibitive for smaller investors. And if persistent advantages are found, traders could compete them away over time so that immediate market sentiment becomes the new standard used by all. Or perhaps robots would be designed to flood the Google search engine with false search terms in a new form of market manipulation.

To a great extent, the validity of the authors’ findings depends on whether search term volumes correspond to some sort of observed behavioral reality. A correlation between search term volume and subsequent or prior market performance appears to exist, but it is critical to understand why the original search behavior exists in order to reach a valid conclusion that pushes the findings beyond the spurious.

Using GSVI data from January 2004 through February 2011, the authors compare weekly lagged differences in search volumes for the specified search terms against the average search volume for the same search terms over different review periods. Ordinary least-squares regressions are used to identify relationships between future and past S&P 500 Index total returns and the changes in search volumes. Robustness tests involve vector autoregression and Granger causality models as well as comparisons with changes in the American Association of Individual Investors’ individual investor sentiment measure, a much-studied control variable. Notably, the period of study includes the Great Recession and two recoveries, which the authors believe provides them with different test environments. They note their inability to test the term “market rally” over one early period because of a lack of data. Their results do not indicate multicollinearity in the regressions.

Among the four search terms, the strongest directional relationships the authors find are for the terms “market crash” or “market rally.” (Results for “bear market” are mixed, and “bull market” has no significance.) The authors interpret these results as evidence that current investor attention relates to prior sentiment measures and can translate into future trading activity, especially when attention turns negative. But near-term past S&P 500 returns also seem to influence current market attention, although somewhat differently. After negative returns, searches for both “market crash” and “market rally” increase, which is perhaps explainable by the “hot hands” effect (i.e., attention follows the observed trend) in the first case and “gambler’s fallacy” (i.e., attention anticipates reversals) in the second.

The authors expand earlier research related to immediate investor sentiment by analyzing Google search terms. Intuitively, investor sentiment should affect internet searches, and the authors demonstrate a relationship between certain terms and market movements. But they do not establish why they expect people to search for the terms they choose in the first place. It seems odd to call out these directional terms as indicative of market sentiment when investors might instead search for “commentary,” “recap,” “business news,” and the like. To add more weight to this study’s results, the authors should demonstrate why they expect specific search terms.