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Crowdsourced stock site Estimize adds M&A predictions (and $1.5 million in venture funding)

By
Erin Griffith
Erin Griffith
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By
Erin Griffith
Erin Griffith
Down Arrow Button Icon
March 26, 2014, 3:25 PM ET

FORTUNE — Wall Street analysts get paid a lot to predict a company’s earnings and stock price. But a startup called Estimize is proving that amateurs working for free can do a better job. All they have to do is band their resources together.

Estimize parses the stock predictions of 25,000 users, half of whom are amateur investors, to come up with an estimate of a company’s earnings per share and, more importantly, the way investors will trade on that news. The platform also includes insights from 3,000 professional buy-side analysts; more than 140,000 people view Estimize’s data each quarter. A recent Deutsche Bank (DB) report declared that Estimize was more accurate than professional analysts two-thirds of the time.

With a small team of 10 and not much in the way of outbound sales, Estimize has secured a handful of clients who pay to access its API. Twenty more are testing the data now. The company is also in talks to feed its data to a few financial media platforms. Estimize’s revenue has grown 100% each quarter, says CEO and founder Leigh Drogen.

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Last year, Estimize introduced private companies to its platform, focusing on a handful of large, pre-IPO startups. Users can now predict the revenue and IPO prospects for startups like Square, Airbnb, Hulu, Etsy, Palantir, Pinterest, and Dropbox.

Today, Estimize introduced the same thing, but for mergers and acquisitions. Drogen admits the site, called Mergerize, may turn into a hotbed for rumor and speculation. But accuracy of rumors isn’t necessarily as important to stock traders as correctly predicting the way the stock market will react. He says that only a third of stories in the Wall Street Journal about pending mergers end up happening. Estimize’s predictions may be worse than that, considering corporations rarely give detailed guidance on M&A activity. But they’ll at least give traders a sense of the market’s reaction to such activities.

Estimize is not the first crowdsourced prediction market to emerge from Web 2.0. The most famous such prediction site was Intrade, which allowed users to place bets on non-sporting events, like elections. The site shut down in 2013 after experiencing a cash shortfall. Estimize is different in that it doesn’t allow users to buy and sell stocks through its platform — there are plenty of other places to do that.

To fuel its expansion, Estimize has raised $1.2 million in new funding at a valuation four times its $1.18 million Series A round from August 2012, Drogen says. The new funds come from existing investors Bob Greene and Jim Savage from Contour Venture Partners, and Longworth Venture Partners. The company also took on capital from a syndicate on AngelList led by ValueStream Labs, with individual angels Brian Finn, former CEO of Credit Suisse; Mike Towey, director of research at Susquehanna; and Jason Finger, the founder of Seamless (GRUB).

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Drogen says he raised capital on AngelList because, as the CEO of a crowdsourced site, he wanted to experience crowdfunding firsthand. “Our platform is reliant on a network effect, and we want as many people as possible who use Estimize to be incented to grow our community with us,” he wrote in a blog post announcing the deal. Since crowdfunding from non-accredited investors is not yet legal, he had to settle for AngelList syndicates. (The JOBS Act legalized crowdfunding for all types of investors, but has not yet finalized rules for implementing it beyond accredited investors.)

Like crowdfunding, Estimize works best when it’s as broad as possible. Consensus reports from Estimize are more accurate, on average, when they include predictions from more non-professionals than when only the professional analysts’ opinions are included. “The theory is when you have a wide distribution of biographies, it’s better than just the industry experts or hedge fund guys,” Drogen says.

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By Erin Griffith
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