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Estimize Data Feed

Integrate Estimize data into your financial models. Programmatically. Real-time.

Top investment professionals around the globe use the Estimize earnings and revenue estimates to benchmark their expectations against their peers, manage risk, and execute alpha generating strategies.

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.

Our API returns data in an easy-to-use JSON format, powering your models with expectations updated with live market information. We also are able to send nightly batch data in a flat file and can discuss other formats to fit in with your workflow.

We offer individual licenses on a per-seat basis, and licenses for institutions on a desk-by-desk or firm-wide. Please submit your contact information via the form on the left. We will be in touch shortly.


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.

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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.

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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.

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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.

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Check out how quants on Quantopian generate alpha with Estimize data.

Check out how quants on Quantopian generate alpha with Estimize data.