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Twitter helps investors

predict the stock market

The millions of messages sent daily via Twitter can help predict moves in the Dow Jones by signalling sentiment, researchers at Indiana University and the University of Manchester have found. 




By scouring tweets for key words and analyzing them using an algorithm they developed to define the mood of Twitterers, Johan Bollen, Huina Mao and Xiao-Jun Zeng said they were able to predict the daily up and down movements of the Dow during a period in 2008 with 87 percent accuracy. Now they are testing whether their results can be applied to real-time data.


“So far we have observed that some of the parameters may change over time but that our conclusion that Twitter mood data can predict fluctuations in the stock market will stand,” said Indiana University’s Bollen in an e-mail interview. “We feel that we have merely uncovered the tip of the proverbial iceberg.”


The research suggests that Twitter analysis could provide a cheap way to improve results of other computer models that use news events and other publicly available data to anticipate how investors will act. The data may even predict to some extent the magnitude of stock market moves, Bollen said.

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