Google Search Proves to Be New Word in Stock Market Prediction
Searches of financial terms on Google can be used to predict the direction of the stock market, according to an analysis of search engine behavior stretching back nearly a decade.
The research, by U.K. and U.S. academics, is the latest attempt to mine online behavior patterns for clues about future movements in financial markets.
The findings appeared to show that people do more searches on terms such as "stocks," "portfolio" and "economics" when they are worried about the state of the markets, said Tobias Preis, associate professor of behavioral science and finance at Warwick Business School.
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Rises in search volumes for such terms are generally followed by stock market declines, according to the research published in the journal Scientific Reports. By contrast, a fall in financial searches often points to greater optimism among investors, leading to a rising market.
With the power of hindsight, trading on the basis of Google search volumes would have led to significant investment gains, Mr Preis said. A short-term trading strategy constructed around searches for "debt", for instance, would have returned 326 per cent between 2004 and 2011.
Google releases data each week showing the volume of searches for specific keywords, providing the raw material for the analysis. The increasing availability of large data sets has given rise to a rash of "big data" attempts to forecast financial markets, although there is little evidence yet of such efforts yielding profits in the "real" world.
Much of the experimentation has revolved around trying to deduce market sentiment from comments on social networks. However, a hedge fund set up to trade on information about market sentiment revealed on Twitter closed after only a month.
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Mr Preis warned that the findings might not hold for future stock market movements. Revealing the predictive value of search data could change people's behaviour, neutralising the effect shown by the analysis.
The work was funded by a US government programme set up to study the predictive power of many different types of data. Mr Preis said his group was in discussions with several investment groups about practical uses for their research.
A large amount of "noise" in the search data made it hard to isolate individual words that would have predictive value. For instance, the volume of searches for "colour" and "restaurant" appeared to be better guides to future stock movements than financial terms such as "Dow Jones" and "markets".
However, the researchers said they honed their sample by checking the daily frequency of a set of financial terms in the Financial Times.