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Estimize Proprietary Analytics
Integrate Estimize data into your financial models & investment process
TOP DISCRETIONARY INVESTORS benefit from Estimize Edge, which screens on over 50 data points based on its unique earnings data set. Use it to generate ideas, monitor watchlists, find stocks with the largest difference in consensus expectations, and manage risk.
WORLD CLASS SYSTEMATIC INVESTMENT PROFESSIONALS around the globe use the Estimize earnings and revenue estimates to execute alpha generating strategies. Our API returns data in an easy-to-use JSON format, powering your models with expectations updated with live, or nightly batched market information.
ESTIMIZE ALSO SERVES ECONOMIC INDICATOR DATA providing the most up-to-date economic expectations data for 25 key measures including Commodities, Employment, Prices, Production, Housing, and more.
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Wang et al. - Deutsche Bank Markets Research
The more timely Estimize forecasts provide greater short-term accuracy when compared to IBES, while IBES estimates do a better job for longer-term forecasts. Specifically, we find Estimize is more accurate than IBES for estimates taken one-week before the announcement date, while the sell-side estimates from IBES show greater accuracy for estimates collected one-month prior to announcement. We find that the timelier Estimize forecasts can more accurately identify earnings surprise which results in a greater capture of the post earnings drift. We use this finding to construct a daily trading strategy that goes long the stocks that beat the Estimize consensus and short the stocks that miss.
Leigh Drogen and Vinesh Jha - Estimize, Inc.
In our paper, “Generating Abnormal Returns Using Crowdsourced Earnings Forecasts From Estimize” we examine consensus EPS and Revenue forecasts derived from the crowdsourced community Estimize, and find that they are more accurate than traditional Wall Street equity analysts’ consensus forecasts. We then design a profitable strategy which trades on earnings surprises as benchmarked against Estimize. Finally, we demonstrate that a strategy which exploits the differences between the Wall Street and Estimize expectations prior to earnings dates earns excess returns, particularly among large cap stocks.
Rick Johnson - Rice University
We examine new forecast data from buy-side and independent analysts collected by Estimize, an open crowdsourcing platform. We compare these forecasts to those of sell-side analysts covering the same firms, found on IBES. The results show that announcement-period stock returns are more strongly associated with the signed earnings surprise calculated using buy-side and independent (Estimize) forecasts. Our study also has potentially broader implications. 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.
Biljana N. Adebambo and Barbara A. Bliss - University of San Diego
We use a novel dataset containing earnings forecasts from buy-side analysts, sell-side analysts, and individual investors, to examine whether the crowdsourcing of earnings forecasts provides value-relevant information. Consistent with the 'wisdom-of-crowds' effect, crowdsourced earnings consensus is more accurate than the I/B/E/S consensus. The accuracy of the crowdsourced consensus increases with diversity. The crowdsourced 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. A trading strategy based on the difference between the consensuses yields an abnormal return of 0.592% per month.
Zhi Da and Xing Huang - Harnessing the Wisdom of Crowds
By tracking user viewing activities on Estimize, we monitor the amount of public information a user viewed before she makes an earnings forecast. We find the more public information viewed, the more she will underweigh her private information. While this improves the accuracy of each individual forecast, it reduces the accuracy of the consensus forecast since useful private information is prevented from entering the consensus, consistent with herding. We also find such a herding behavior to become more severe if the public information set contains estimates of the “influential” users. Finally, to address the endogeneity concerning the information acquisition choice, we collaborate with Estimize to run experiments where we restrict the public information set on randomly selected stocks and users. The experiments confirm that “independent” forecasts lead to more accurate consensus. Overall, our findings suggest that wisdom of crowd can be better harnessed by encouraging independent voice from the participants.