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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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.
The Wolf Research team found that an Earnings Yield strategy replacing Thomson Reuters I/B/E/S estimates with Estimize generated 1,000 basis points of alpha per year compared to the Russell 3,000 index.
In their paper, the Wolf Research team constructs several risk mitigation and enhanced value strategies, stating, "For long-term value investors, we show how Estimize data can be used to boost performance. We also overlay our enhanced value strategy with a low risk tilt (by avoiding earnings uncertainty) to further improve return and reduce risk."
From "More Accurate and Timely Estimates Lead to Better Investment Strategies"
McKinley Capital Research the negative deviation stocks underperformed the S&P 500 by 168 basis points (“bps”) on an unweighted basis and -11 bps on a weighted basis.
The combined results of the pre-announcement and post-announcement effects, suggest several ways that advisers might use “crowd-sourced” earnings estimates. Avoid owning stocks when the “crowdsourced” earnings estimate is first observed to be significantly below street estimates. Hold, or consider buying stocks with significant positive earnings surprise — especially when the actual earnings number exceeds the “crowd-sourced” estimate. Sell earnings misses with extreme caution! Contrarians might even consider buying on initial down moves in stocks with earnings misses!
From "Improving Earnings Forecasts with Estimize Crowd Sourced Data"
The Deutsche Bank Quantitative Research team "On A Post Earnings Announcement Drift Strategy"
Deutsche Bank Quant Research found multiple benefits to using the Estimize dataset; especially in short term applications where accuracy is essential. The diversity of contributors provides a greater spectrum of information which improves investment strategies.
University of San Diego, Biljana Abebambo & Barbara Bliss "On More Accurate Estimates And True Market Expectations"
University of San Diego researchers found that 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 expectation.
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 "The Value of Crowdsourced Earnings Forecasts"
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. We find evidence that the incremental usefulness of Estimize in forecasting earnings and proxying for the market’s expectation increases with the number of contributors. This illustrates that the value of crowdsourcing is a function of crowd size.
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?”
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