There’s nothing revolutionary about gauging consumer opinion, except when you use social media to help. Incorporating properly distilled data from the likes of Twitter and LinkedIn offers investors access to an exponentially broader slice of the population.
Imagine going from a survey of hundreds or thousands of people to analyzing data volunteered by millions. It’s like an aggregator on steroids. And that
A 2010 study in the Cornell University Library found that “collective mood states derived from large-scale Twitter feeds” predicted swings on the Dow Jones Industrial Average with 86.7 percent accuracy. Researchers
used two mood tracking tools -- similar to what traders could widely employ someday -- to gauge the collective disposition of microbloggers.
“Including this mood information leads to higher accuracy,” computational social scientist Johan Bollen told
Wired following the study. “We’re presuming on the basis of what we found, if you have some kind of super-duper algorithm and you add our time series, its accuracy will go
And go up it has -- at least once. A hedge fund with London-based
Derwent Capital Markets beat the market last summer by tallying and categorizing keywords found on Twitter such as “alert,” “happy” and “vital,” according to
The Atlantic Wire.
Drilling Down to Individual Stocks
Derwent’s approach reflects a technology in its infancy. It doesn’t try to figure out why the tweets are positive or negative. That makes it suitable only for trading broad market indices such as the Dow.
social networks still isn’t the best way to predict an individual company’s stock moves. That requires traditional vigilance over SEC filings, the CEO’s state of mind and impending scandals because most social media users don’t brag about their high-performing
stocks on Facebook or Tweet about their latest bargain equity.
But they do jabber at length (pun intended) about consumer products via blogs and YouTube, not to mention exposure on Flickr. That opens the door to sophisticated indirect sentiment analysis that may someday predict swings
in individual stock prices.
This technique may not be as helpful if you’re tracking the manufacturer of the latest classified military fighting vehicle. But it could be great if you’re watching the manufacturer of the latest smartphone or tablet computer -- or the big-box retailer
that sells them.
Spotting Corruption, Risk
Monitoring social networks can also spot patterns among seemingly random events, which in turn can expose fraud. Deploying a platform with the right mix of analytical technologies offers firms the ability to uncover market manipulation and respond accordingly.
“[If the technology] is too far off for trading, maybe we would want to look at social media for risk management,” Peter Van Kleef, managing director at Starnberg, Germany-based consultancy
Lakeview Capital Market Services told
CNBC.com last week. “If we can just find a couple of those time bombs before they go off, we can reduce our exposure to them.”
That means climbing mountains of unstructured data; and social media tracking technology is still learning how to walk. But early adopters are already breaking in their boots, likely to work out the bugs to reap the juiciest data -- and create formidable
buddy lists along the way.
Even if capital markets firms don’t take up social media tracking en masse, many companies have already started tracking themselves. Facebook, blogs and discussion groups are a few ways that an enterprise can manage its image online.
This image management has evolved from individuals monitoring the Web to automated reports to real-time aware businesses. And it represents a tremendous opportunity for capital markets firms to develop this technology in parallel.
So whether starting small with risk or going big with trade modeling, this is a budding technology with a lot of promise. As we have seen time and time again, failure to adapt soon enough could turn even today’s mightiest firm into the MySpace of trading