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financial modeling, computation, twitter


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Financial Modeling with Twitter

University of California, Riverside researchers have developed a model that uses Twitter data to help predict the traded volume and value of a stock the following day. The model, developed by Riverside professor Vagelis Hristidis, outperformed other baseline strategies by as much as 11 percent, and did better than the Dow Jones Industrial Average during a four-month simulation.

The research focused on the volume of tweets and the ways that tweets are linked to other tweets. The researchers obtained the daily closing price and the number of trades from Yahoo! Finance for 150 randomly selected companies in the S&P 500 Index for the first six months of 2010. They then developed filters to select only relevant tweets for those companies during that time period. The researchers found that the number of trades is slightly more correlated with the number of connected components, which is the number of posts about distinct topics related to one company. The researchers also found that stock price is slightly more correlated with the number of connected components.

Full Article...

Source: ACM TechNews


Vagelis Hristidis (University of California, Riverside)
Eduardo J. Ruiz (University of California, Riverside)
Carlos Castillo (Yahoo! Research Barcelona)
Aristides Gionis (Yahoo! Research Barcelona)
Alejandro Jaimes (Yahoo! Research Barcelona)

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