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Quantitative Investors

Learn how you can generate alpha with these proven systematic strategies.

Discretionary Investors

Don't suffer from informational asymmetry. Be better informed than your peers.

Academic Researchers

Use our unique data set to study the wisdom of crowds and investor behavior.

Media Partners

Offer your viewers the most representative earnings expectations data set.

In the last 3 years, we’ve seen top research teams publish 8 groundbreaking papers with many more in the works across accounting, finance, and behavioral economic disciplines.

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Study the only crowdsourced earnings data set…

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To take a second look at our understanding of estimates

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To study the wisdom of crowds effect

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To explore new areas of behavioral economics and asset pricing theory

The Sell Side estimate data set is a highly skewed and flawed sample of expectations. Much of the academic literature needs to be rewritten using the more accurate and representative data set from Estimize. Several legacy papers have already been replaced, but much more is left to be done.

Research into the wisdom of crowds is still nascent. We’ve worked side by side with academics to run experiments on Estimize to look at several aspects of this theory including sample size, independence, herding, and biographical backgrounds.

Our data set contains information regarding investor attention and behavior on the Estimize platform. Work is underway to understand how investor behavior correlates with decisions and outcomes in the market, as well as how investor attention affects market prices, volatility and liquidity.

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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