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Contact Jeff Bercovici
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Twitter Chatter Can Predict Stock Prices, Study Finds

Could this bird be the key to beating the market?

Celebrating its sixth birthday yesterday, Twitter revealed that it now has 140 million users generating a collective 340 million posts a day. That’s a lot of data with a lot of patterns hidden in it, just waiting to be teased out.

Does one of those patterns hold the key to predicting trends in stock prices? It appears so, according to a study published this week by researchers at the University of California, Riverside.

A team led by Vagelis Hristidis, an associate professor of computer science and engineering, set out to discover the relationship, if any, between what people are tweeting about publicly traded companies and what’s happening with those companies’ shares. They were not explicitly looking for a way to predict changes in stock price, says Hristidis.

“In the end, however, we found there was some potential to do that,” he adds.

To tease out the connection, Hristidis and his team zeroed in on a variable they dubbed connected components. That refers to the number of distinct conversational threads taking place around a company in any given day. For instance, if the company in question is Apple, there might be separate clusters of tweets focusing on the iPad, the iPhone, Steve Jobs and the Foxconn factory.

The more connected components, they found, the higher the volume of trading for that stock in subsequent days, they found. They also identified a positive correlation between the number of connected components and price: more clusters tended to predict a price increase while fewer clusters predicted a loss. But that relationship was weaker than the one between connected components and volume of trading.

Using their findings, Hristidis’s team developed a trading model designed to take advantage of price changes reflected by Twitter. Over a four-month trial period, their model outperformed the Dow Jones Industrial Average (a 2.2% loss for the Twitter model versus a 4.2% loss for the Dow) and several other trading algorithms. Since this is the only period the researchers looked at, they caution that it’s possible their model would perform differently during a bull market.

Why should there be any relationship whatsoever between a stock’s price and the number of distinct conversations around that stock on Twitter? “That’s an interesting point,” says Hristidis. “To be honest, I’m not sure why we found this correlation.” He hypothesizes, however, that conversations tend to coalesce around a piece of bad news, while good news is less exciting to dwell on, leading to multiple digressions.

Hristidis says he hasn’t been in touch with any financial institutions about incorporating his findings into an actual trading model. “Realistically, it would just be one more signal for trading strategy,” he says.

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