“Estimates are incredibly important,” Drogen said, “because it’s how we build an understanding of what we expect the stock to be worth (down the road).”
Related Link: How Wall Street Is Using Estimize
Crowdsourcing Not Unique
Relying on crowdsourcing for estimates, however, is not what makes Estimize unique, according to Drogen.
“Crowdsourcing has been (part of) Wall Street forever,” Drogen said. “When traders were in the pit or on the floor they would share information with each other.”
That, he explained, was crowdsourcing, albeit with a “very narrow group of people.”
According to Drogen, “The innovation of Estimize is that identity does not matter.”
This flies in the face of conventional Wall Street wisdom, which relies on identity and reputation to establish trust.
Estimize, on the other hand, uses statistics to validate the quality of the information, thereby removing the need for identity.
Vetting Estimize Style
Even though users don’t know the identity of contributors, Drogen noted, Estimize collects a lot of information on each contributor in order to vet the quality and accuracy of the data they provide.
“We collect structured biographical data that does not personally identify the contributor.”
Are they amateur or professional? If professional, are they buy side, sell side or independent?
If amateur, what sector do they work in? What industry within that sector?
Are they an academic or student?
It’s all about establishing the quality of the estimate.
“We are not interested in the biggest mutual fund analyst,” Drogen said. “We are interested in the best mutual fund analyst.”
Estimize recently introduced a way to share data, including the Estimize consensus, on a variety of platforms.
Everything, to that point, was aimed at “connecting all of the estimate data to price,” Drogen said. “We’re going to do that two ways.”
Related Link: How Crowdsourcing Is Democratizing The World Of Finance
Converting Data To A Signal
“First, we’ll take all the raw estimate data we have been selling to quantitative hedge funds, put it into our own signal and make it available through the front end of the Estimize platform,” he noted.
Drogen described a scale from -100 to +100 in which a positive reading of 100 would be a buy signal based on estimates indicating the stock would rise.
A negative 100 reading would indicate the stock would go down, suggesting a short position.
Zero would simply indicate there was no signal associated with Estimize data.
The second way Estimize planned to connect data to price would be to show what the stock price would be in the future.
Those estimates would be based on community estimates and whatever multiple the community believed the stock would trade at.
At the time of this writing, Jim Probasco had no position in any mentioned securities.
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