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(Richard Drew | The Associated Press) Given the frenzy over social media, it was only a matter of time before hedge funds and other Wall Street trading shops tried to cash in. Unlike individual investors who look for slow and steady returns, these traders thirst for any sort of an edge in a lightning-fast market.
When it comes to tips, stocks pros turn to Facebook, Twitter
Investing » Critics aside, many see link between publicsentiment and prices.
First Published Apr 07 2012 01:01 am • Last Updated Apr 09 2012 09:41 am

The next big stock tip might be as close as a Twitter feed.

Professional traders are developing computer programs that scour Internet posts in search of the next stock-market darling. Their technology analyzes everything uttered about a company — good or bad, racy or mundane — and spits out trading recommendations.

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The logic is that the popular sentiment expressed by millions of people on sites such as Twitter and Facebook yields investment clues that can’t be gleaned from financial statements or analyst reports.

Complaints about a new product can foreshadow a drop in sales and, hence, in a stock. Internet posts also can showcase investor emotions, such as when panic might cause people to irrationally dump a stock.

Given the frenzy over social media, it was only a matter of time before hedge funds and other Wall Street trading shops tried to cash in. Unlike individual investors who look for slow and steady returns, these traders thirst for any sort of an edge in a lightning-fast market.

"Any time you can predict the future, there’s money to be made," said Richard Peterson, whose Santa Monica, Calif.-based MarketPsych sells social-media data to hedge funds and banks.

Sound far-fetched? Plenty of skeptics, including a fair number on Wall Street, question whether any of this actually works. And even if it does, it would be Wall Street professionals who benefit rather than people actually using the sites.

For example, on Jan. 18, Peterson’s computers recommended buying semiconductor maker Spreadtrum Communications Inc. based on comments on a Twitterlike site for investors. The shares had fallen sharply, and several investors said they might buy the stock if a fledgling recovery could hold.

"So tempting to jump back in," one investor wrote.

Peterson’s computers read that as a bullish sign for anyone who could jump in first. A trader buying 1,000 shares at the next day’s opening would have made more than $500, because the shares rose nearly 4 percent in a week.

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Doubters scoff at the idea of trading based on unfiltered rants in the blogosphere.

It reminds John Buckingham, chief investment officer at Al Frank Asset Management Inc. in Aliso Viejo, Calif., of the late-1990s dot-com bubble. He would mention stocks on CNBC, he said, and even those he recommended selling would surge.

"The stock chart would go up [on the screen] and people would buy it," Buckingham said. "Investors should focus on quality companies, not what somebody is saying in a chat room or on a social-media site."

Even social-media practitioners acknowledge how unorthodox their strategy is.

"I openly accept that there are a lot of eye-rolls out there," said Randy Saaf, co-founder of AlphaGenius Technologies in Los Angeles. "There’s a long path ahead of us to get people to shift away from what we’ve been taught by Warren Buffett."

Peterson admits his method doesn’t always work — a recommendation to buy water company Heckmann Corp. on Jan. 26 backfired when the stock fell 11 percent by the time the sell signal came a few days later.

But he views social-media investing as a promising strategy in an industry that has long looked for secret formulas to trading stocks. MarketPsych, AlphaGenius and other companies sell customized data feeds to hedge funds, high-frequency traders and other professionals.

A handful of studies have shown a correlation between sentiment on social-media sites and the performance of stock prices.

Johan Bollen, a professor at Indiana University, found that Twitter posts correctly predicted the direction of the Dow Jones industrial average 86.7 percent of the time.

His study analyzed almost 10 million tweets by 2.7 million users over a nearly 10-month period in 2008. It found that users’ moods one day presaged the direction of the Dow Jones industrial average three days later.

"There’s a lot of promise," Bollen said. "It’s really tantalizing to be able to tap into the global zeitgeist."

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