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Jun 05, 2013

Estimize offers new approach to consensus estimates

Crowdsourced earnings estimates platform wins approval from Bloomberg

Leigh Drogen, founder and CEO of Estimize, the first open platform for earnings estimates, says ‘it is not just improved accuracy – although we are [more accurate] about 67 percent of the time – but the better representation of the true expectation of the market that most interests the IR departments we speak with.’

Estimize, which in April received the endorsement of securing a licensing agreement from Bloomberg, was born through Drogen’s belief in the inherent limitations of sell-side estimates. He devised a new method to create a more accurate and representative consensus expectation, while identifying the most accurate analysts.

The 26-year-old New York native invited anyone to contribute earnings estimates, including buy-side clients from hedge funds and independent investors, while also drawing from the pool of sell-side analysts who contribute to the Wall Street consensus. Attracting more than 6,000 participants, Estimize then aggregates the predictions and posts them next to the Wall Street consensus forecast.

That Bloomberg was able to commit to launching Estimize on its website was ‘a huge stamp of approval for the open philosophy we have,’ says Drogen, who describes Bloomberg as a company that doesn’t get into things lightly, and tends to vet things thoroughly.

‘We realized the viewpoint of sell-side analysts – given their position in the world – isn’t really lined up to be as accurate as possible,’ explains Drogen. ‘Their job is not really to produce accurate estimates, but rather to fit them into the investment banking and corporate access businesses.

‘Given the crowdsourcing theory – if you have a number of different expectations from a broad range of individuals, you’re going to get better-condensed, more accurate consensus – we realized that if we could get hedge fund guys, the independent analysts, the independent traders and investors and the corporate finance professionals who have interesting viewpoints into their own industries to all contribute to the dataset, the figures would be very powerful.’

Drogen broke into the finance world as a trader with Geller Capital before founding Surfview Capital, a New York-based investment management firm. He believes there is a negative ‘religiosity on Wall Street that has been pervasive for many, many years. The guy with the biggest megaphone gets to control the thought. The megaphone is really the name on your door, be it Morgan Stanley, Goldman Sachs or JPMorgan.

‘I liken this to the Moneyball. A scout goes out and he says, That guy looks good. He’s got big muscles and a good swing. But he doesn’t actually look at the statistics. Then along comes the sabermetric statistics [a form of stats used in sports performance analysis] way of looking at things, which doesn’t look at the actual background or physical make-up of the player, but looks at what he or she has done.

‘I think this is one of the really difficult things for Wall Street to get its head around: it isn’t about the name on your door or how important your firm is. We only care if we can validate the estimates you are making and whether they are accurate and done in a reliable manner.’

Currently Estimize has more than 2,600 buy-side and independent analysts contributing estimates to more than 900 stocks each quarter. But the deal with Bloomberg, which lifts Estimize’s users from 3,000 to Bloomberg’s community of 300,000 finance professionals, has not drastically altered Drogen’s approach.

‘We’re a team of seven right now in New York,’ he notes. ‘Our dataset is expanding pretty rapidly – we grew by more than 100 percent, quarter over quarter, last year. The goal as a company right now is to grow that dataset as large as we can and [expand] the number of contributors and the number of people using it.

‘Eventually we’ll expand into other datasets and business line items, such as iPhones and iPads for all the companies. But right now our focus is the growth of the community and the growth of the dataset.’

 

 

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