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"We found multiple benefits to using the Estimize dataset; especially in the case of short term applications in which accuracy is essential."
—Deutsche Bank Quant Research


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The world’s top systematic investors use our data to generate alpha in a variety of ways...

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Pre Earnings Announcement
Drift Strategy

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Post Earnings Announcement
Drift Strategy

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Volatility
Strategy

In the pre earnings period, the Estimize Consensus is a far superior representation of true market expectations due to the size, diversity and frequency of engagement of Estimize contributors. This means you can arbitrage the delta between the Estimize and Wall Street Consensus in the two week period leading into each earnings report.

The more representative Estimize Consensus can greatly improve the classic post earnings drift strategy used by systematic quants over the past 25 years. We found 65bps of residual return in the five days post earnings when there are large beats and misses of the Estimize Consensus. You can also identify potential misses, which produce negative price reaction historically and create volatility in certain stat arb strategies.

Due to a more honest dispersion of expectations within the Estimize data set relative to Wall Street, you can identify mispricing in vol prior to an earnings report. Larger dispersion of estimates for a given stock relative to that stock's previous reports results in more realized vol in the post earnings period than the implied vol.

Our data has been validated by top quantitative and academic research teams

The Deutsche Bank Markets Research Team On A Post Earnings Announcement Drift Strategy


The Deutsche Bank Quantitative Research team found that their post earnings announcement drift strategy using the Estimize Consensus produced 65 basis points of residual return in the five days post announcement (for beats and misses of at least 10%).

In confirming the superior accuracy and representativeness of the Estimize data set as compared to Thomson Reuters I/B/E/S, Deutsche Bank says, "We found multiple benefits to using the Estimize dataset; especially in the case of short term applications in which accuracy is essential. The diversity of contributors provides a greater spectrum of information which can improve investment strategies."

From "The Wisdom of Crowds: Crowdsourcing Earnings Estimates"

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University of San Diego, Biljana Abebambo & Barbara Bliss On More Accurate Estimates And True Market Expectations


University of San Diego researchers found that "all users, even Non-Professional users, contribute to making the Estimize earnings consensus more accurate. The consensus accuracy increases with the number of Estimize forceasts, and more importantly, the diversity of contributors."

Consistent with the 'wisdom-of-crowds' effect, Abebambo and Bliss found that the Estimize consensus is more accurate than the I/B/E/S consensus, and that the accuracy of the crowdsourced Estimize consensus increases with diversity. As such, the Estimize consensus “produces errors that are more strongly associated with abnormal returns, suggesting that it is a superior measure of the market’s true earnings expectations.”

From "The Value of Crowdsourcing: Evidence from Earnings Forecasts"

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Leigh Drogen & Vinesh Jha, Former Head of Starmine Quant Research "Generating Abnormal Returns Using Crowdsourced Earnings Forecasts from Estimize"

In this white paper, the internal Estimize quantitative research team outlines the data available as well as several alpha producing systematic strategies which are easily tested and put into production.

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Rice University, Rick Johnston "Crowdsourcing Forecasts: Competition for Sell-Side Analysts?"

"Estimize is a market solution to the inherent bias of sell-side analyst forecasts, which produces more reliable and timely estimates due to its size and diversity."

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Michigan State University, Zhi Da and Xing Huang “Harnessing the Wisdom of Crowds”

The findings suggest that the Estimize give-to-get model prevents herding behavior and encourages participants to share their independent expectations thereby delivering a more accurate consensus.

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University of Kentucky, Russell Jame "Does Crowdsourced Research Discipline Research Analysts"

The findings suggest that the more accurate and less biased Estimize Consensus has had a reflexive effect on the accuracy of sell side analyst estimates in recent years. Competition from Estimize seems to be driving this behavior.

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The most trusted source of earnings estimates

Rick johnston

Estimize as a crowdsourcing platform represents a market solution to the shortcomings associated with sell-side analyst forecasts perhaps resulting from their incentives. The application of technology to enhance the information environment of firms is innovative and possibly revolutionary.

Rick Johnston, Rice University, “Competition for Sell-Side Analysts?”


Our estimates are quoted by leading media publications and tv networks…

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