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Can Google Predict Stock Market Events?

While the movie, Terminator 2, evoked the horrifying reality of a world run by self aware robots that were bent on exterminating humanity, according to recent data that was published in Scientific Reports, search engines and their complex algorithms are now capable of predicting stock market crashes, and with a very precise degree of accuracy. While it’s certainly quite some time in the future where we may even have to ever worry about computers becoming “self aware” as they did in the infamous Arnie action shoot-up, new reports show that Google can, and has, predict stock market crashes and even a Bull Market.

“Google Trends data did not only reflect the current state of the stock markets but may have also been able to anticipate certain future trends,” the report stated. “Our findings are consistent with the intriguing proposal that notable drops in the financial market are preceded by periods of investor concern. In such periods, investors may search for more information about the market, before eventually deciding to buy or sell. Our results suggest that, following this logic, during the period 2004 to 2011 Google Trends search query volumes for certain terms could have been used in the construction of profitable trading strategies.”

Oddly enough, Google has not capitalized on this trend… just yet. One has a good mind to think that as more reports like these come to light, that the big wigs over at Google might just consider getting into the stock trading business. With social trading (a form of stock trading that merges popular social media functions with online stock trading) becoming rampantly popular and successful in Europe, it’s a ripe market over here in the U.S. for a new player in the stock trading game online (Forex).

The researchers analyzed various search terms that were relative to stock trading trends from 2004 to 2011. Many of the 98 terms researched were actually suggested by the Google Sets service. The report found that there were notable increases in the prices of the DJIA, which were predicated by a decreased search volume overall for certain financially relative terms, and that this correlated directly with a declining price model for the DJIA; and vice versa.

“Our trading strategy can be decomposed into two strategy components: one in which a decrease in search volume prompts us to buy (or take a long position) and one in which an increase in search volume prompts us to sell (or take a short position),” the report said. “We detect increases in Google search volumes for keywords relating to financial markets before stock market falls,” the report read. “Our results suggest that these warning signs in search volume data could have been exploited in the construction of profitable trading strategies.”

The report concluded that: “We conclude that these results further illustrate the exciting possibilities offered by new big data sets to advance our understanding of complex collective behavior in our society.”

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