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Can you beat the stock market in 140 characters or less?

The financially hapless author of that tweet won't make a dime from his post -- but someone might.

At least that's the working premise for a growing number of hedge funds, banks, asset managers and other investors that scour social media for information they can use to guide investment decisions. Twitter is viewed as a particularly rich trove, a perpetual real-time ticker of news events, corporate announcements, government reports, scientific findings, idle musings by investors and many other kinds of potentially market-moving data.

Technology firms like Dataminr today analyze hundreds of millions of tweets a day in order to detect events made public over Twitter long before they come to the attention of news organizations (Disclosure: Dataminr is also used by many media outlets, including CBS News.) And Twitter itself recognizes its own potential value for playing the stock market. That's why in 2014 it bought Gnip, which helps financial professionals track and analyze social-media data.

"As Twitter increasingly grows as a source for breaking news, Dataminr for Finance delivers a relevant stream of content to financial professionals based upon their personalized portfolio of tickers, sectors and macro topics," said Dataminr founder and CEO Ted Bailey, alluding to the company's product geared to financial firms. "This provides financial users with a new source of alpha, as well as key insights, off-the-radar context and differentiated perspective."

Derwent Capital, a small London investment firm, helped pioneer the use of Twitter for investing in 2011 when it launched the first hedge fund that analyzes tweets for clues about the stock market. It lasted a month. But the firm's founder reportedly went on to start another hedge fund that also taps social media, claiming that it generated an investment return this year of 9.7 percent, compared with only 1.7 percent for the S&P 500.

Other players also have plowed ahead, bolstered by a growing body of research pointing to the microblogging platform's potential in making investment decisions.

"A positive tweet from a person who invested some of his money in a company may express excitement about the future of that company," said Darko Aleksovski, a research assistant at the Jozef Stefan Institute in Slovenia and co-author of a 2014 study that examined the effect of Twitter chatter on stock prices, by email. "It resembles horse racing: You placed your bet and you're cheering in the stands for your lightning-fast horse. The race might take more time, though."

Thomas Renault, a professor of finance and economics at Pantheon-Sorbonne University in Paris, writes in Quartz that most of the related academic work today focuses on what investors can glean from the feelings Twitter users express about a given company or stock. "Sentiment analysis," as the concept is known, uses algorithms to decide if a tweet is positive, negative or neutral.

The obvious challenge of parsing emotions for actionable trading intel is that feelings are subjective, ephemeral and hard to quantify. If poets and philosophers are still unraveling the mysteries of the human heart, what chance does an algorithm have? A related problem: People lie. As even casual readers of stock bulletin boards can attest, financial markets are a vast ocean of rumor, error and rank disinformation.

Put another way, when even well-meaning Twitter users express how they feel about an investment -- positive, negative or something in between -- it can be hard to separate fact from fiction.

As Renault notes, while there seems to be a correlation between a person's "social feeling" for a given stock and how the broader mass of investors value it, the jury is out on whether that sentiment is what is moving the market. "In other words, Twitter is not a crystal ball that can predict markets, but rather a mirror reflecting the current situation," he wrote.

One way to get around such problems is to analyze huge amounts of social-media data, paying particular attention to clusters of tweets and posts related to a particular event, such as a CEO's abrupt resignation or news of a scientific breakthrough.

"The computational analysis of the moods of social media messages is one way of ascertaining this 'collective wisdom' on a given topic," researchers with the University College London wrote in a 2014 study that explored if social media can anticipate markets.

Aleksovski thinks there really is a relationship between the positive sentiment about a stock culled from Twitter data and a positive movement in its price. Less clear, for now, is whether investors can channel that collective wisdom to move before the market does.

"Investigating on a daily-time scale, we found that the sentiment follows the direction of the market, but we did not find predictive potential," he said. "We are currently investigating on a minute-time scale to see if the predictive potential is there."

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