Estimize’s Earnings Edge and Factor Model will fit seamlessly into your investment process to help you manage risk and identify opportunity around earnings announcements.
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The discretionary funds that know they need to behave in a more quantitative manner around market expectations use Estimize Earnings Edge to play Moneyball. With Edge, you can identify names likely to outperform and underperform based on current and historical data proprietary to Estimize. The Earnings Edge is the only place you can get your buyside peers' expectations all in one place.
Not only is Estimize more accurate than Bloomberg or Thomson Reuters estimates 74% of the time, it more truly represents market expectations. You don't need to guess what number a company has to hit just order to satisfy what's currently priced into the market. Use the Estimize Consensus—crowdsourced from tens of thousands of analysts, it is exactly what you’ve been looking for.
Don't get caught on the wrong side of the pre or post earnings trade. Our quantitative research team has developed a Factor Model based on the strategies we know work for fully systematic investors like you. The Factor Model produces a -100 to +100 score in the 3 weeks around each stock's earnings and generates significant alpha.
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"
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"
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.
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."
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.
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.
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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