The next generation of financial investors will plan their strategy according to Twitter data.
If knowing what people had for breakfast, when they last went to the bathroom, and their favourite links to cats were worth money, we'd all be rich.
Not content with a global economic meltdown caused by loaning vast sums of cash to individuals (and, let's face it, nations) who couldn't make the payments, investors are now looking at Twitter conversations as a new way to lose money.
No, not tub-thumping, linkbaiting hyperbole. Unfortunately. Recent research from University of California, Riverside, examining the use of Twitter data to predict the trading value of investment stock, showed losses of 2.4 percent. But in the current state of the financial market, that's considered a win.
The trading strategy, developed by Vagelis Hristidis, an associate professor at the Bourns College of Engineering, one of his graduate students and three researchers at Yahoo! in Spain, outperformed other baseline strategies by between 1.4 percent and nearly 11 percent and also did better than the Dow Jones Industrial Average during a four-month simulation.
"These findings have the potential to have a big impact on market investors," said Hristidis, who specializes in data mining research, which focuses on discovering patterns in large data sets. "With so much data available from social media, many investors are looking to sort it out and profit from it."
They 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 half of 2010.
Then, they developed filters to select only relevant tweets for those companies during that time period. For example, if they were looking at Apple, they needed to exclude tweets that focused on the fruit.
They expected to find the number of trades was correlated with the number of tweets. Surprisingly, the number of trades is slightly more correlated with the number of what they call "connected components." That is the number of posts about distinct topics related to one company. For example, using Apple again, there might be separate networks of posts regarding Apple's new CEO, a new product it released and its latest earnings report.
Source: University of California – Riverside