The volume of Google searches for finance related terms may predict moves in markets more research suggests.
An investment strategy based on these search volume data between 2004 and 2011 would have made a profit of 326%.
The analysis Quantifying Trading Behavior in Financial Markets Using Google Trends was published in Scientific Reports.
It joins an ever-increasing array of “big data” studies in which aggregated data are beginning to give striking insights into behaviour.
Web searches are increasingly integral to our decision-making, and because of its dominance among search engines, Google data have already proven their worth in big-data studies.
Google’s own researchers found that searches can track the spread of influenza and more recently showed that they “predict the present” with regard to economic indicators.
The use of websites to determine share price movements have been around for a while- 2 years ago our sister blog Search Clinic posted: Twitter is tracked by hedge fund managers for investment news
In the same year- 2011, the Bank of England determined that searches for relevant terms could even predict house prices.
The new report gives hints that straightforward analysis of interest in general finance-related terms can be a good predictor of overall market health.
The team started with a set of 98 search terms and tracked how search volumes on those terms varied over a period between 2004 and 2011, and correlated those with the Dow Jones Industrial Average.
Generally, searches for the most finance-focused terms such as “stocks” and “revenue” went down before rises in that market average, whereas when those terms were searched for more often, the average tended to fall in subsequent weeks.
The team developed a hypothetical investment strategy through the period, buying notional stocks in weeks that financial-term search volume fell, and selling them when volume rose – a strategy that would have gained them a profit of 326%. By comparison, simply buying in 2004 and selling in 2011 would have yielded a profit of 16%.
The researchers have already been approached by executives within the financial industry to try to put their findings to use, and have recently received a grant from the Engineering and Physical Sciences Research Council to develop a “big data” software platform specifically aimed at the emerging business models that will depend on it.