Can Google Searches Predict Stock Price Performance?

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A recent study in the Journal of Finance by Zhi Da and Paul Gao of the University of Notre Dame shows that data from public Google searches can be used to beat the stock market by up to ten percentage points per year. Similar findings were released last month by researchers at the University of Kansas.
The Notre Dame authors argue that the frequency of Google searches received by a stock (its SVI number) is a better, more direct method of measuring investor attention (a precursor to buying the stock) than traditional, indirect methods of measurement, such as news and advertising expense.
Here’s the abstract:

We propose a new and direct measure of investor attention using search frequency in Google (Search Volume Index (SVI)). In a sample of Russell 3000 stocks from 2004 to 2008, we find that SVI (1) is correlated with but different from existing proxies of investor attention; (2) captures investor attention in a more timely fashion and (3) likely measures the attention of retail investors. An increase in SVI predicts higher stock prices in the next 2 weeks and an eventual price reversal within the year. It also contributes to the large first-day return and long-run underperformance of IPO stocks.

The authors argue that search is a “revealed attention measure,” reasoning that if you Google a certain stock, you are undoubtedly paying attention to it. This is a view that Google seems to hold itself, as evidenced by Google Chief Economist Hal Varian‘s recent suggestion that search data have the potential to describe interest in a variety of economic activities in real-time.
The authors find considerable evidence that attention measured by search volume is related to IPO first-day returns.

First, we find that searches related to IPO stocks increase by almost 20% during the IPO week. The jump in SVI indicates a surge in public attention consistent with the marketing role of IPOs documented by Demers and Lewellen (2003). When we compare the group of IPOs that experiences large positive ASVI during the week prior to the IPO to the group of IPOs that experiences smaller ASVI, we find that the former group outperforms the latter by 6% during the first day after the IPO and the outperformance is statistically significant. We also document significant long-run return reversals among IPO stocks that experience large increases in search prior to their IPOs and large first-day returns after their IPOs.

Moreover, less sophisticated investors and individuals are more likely to use a Google search for information related to stocks.

Among our sample of Russell 3000 stocks, stocks that experience an increase in ASVI this week are associated with an outperformance of more than 30 basis points (bps) on a characteristic-adjusted basis during the subsequent two weeks. This initial positive price pressure is almost completely reversed by the end of the year. In addition, we find such price pressure to be stronger among Russell 3000 stocks that are traded more by individual investors. The fact that we document strong price pressure associated with SVI even after controlling for a battery of alternative attention measures highlights the incremental value of SVI. In fact, ASVI is the only variable to predict both a significant initial price increase and a subsequent price reversal.