Google searches predict market moves

By Jason Palmer
Science and technology reporter, BBC News

NYSE trading floor
Image caption,
Stock averages slumped when Google searches of financially relevant terms rose

The volume of Google searches for finance-related terms may predict moves in markets, research suggests.

As the search volume on generic terms such as "debt", "portfolio" and "stocks" fell, the Dow Jones average tended to go up - and vice versa.

An investment strategy based on these search volume data between 2004 and 2011 would have made a profit of 326%.

The analysis in Scientific Reports was based on publicly available data from the Google Trends service.

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.

In 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.

"We were intrigued by the idea that stock market data serves as a really large record of all the actions people take in the stock market, but don't necessarily tell us much about how people decided to take those actions," said Suzy Moat of University College London, co-author of the paper.

"We wondered whether by looking at Google, we could get some insight into some early information-gathering stages of how people make decisions," she told BBC News.

'Clear new opportunities'

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.

Image caption,
Vast tranches of "big data" can shed light on all kinds of collective behaviour

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%.

Tobias Preis of Warwick University, another co-author of the paper, said the link between heightened search activity and dips of the market average bore out the well-known phenomenon of loss aversion.

"People are more afraid to sell something that they already have, rather than buying something," he told BBC News.

"It makes sense and is in line with the scientific concept that there are more efforts to collect information before we see subsequent negative moves on an aggregated scale.

"From a scientific point of view, it's really excellent that... we have really got the technology - or the data based on technology - which makes it possible to look to some extent into early decision-making processes."

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.

But Dr Moat said that more broadly, "big data" was a boon to studies across disciplines, from the financial to the sociological.

"I think it really opens clear new opportunities to understand collective behaviour," she explained.

"Large data sets allow us to look for patterns in people's behaviour which we can then use as a predictions for things that repeat in the future.

"Having these large data sets allows us to look for those patterns on a much more specific basis - obviously it gives us a much larger sample than a laboratory-based experiment, and more accurate information than sending out surveys."

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