Estimize provides the world’s most accurate and representative institutional grade estimate data sets through our real time API, daily FTP, daily Excel files, and web platform. Our data, which is collected from our community of
98214
contributors via our web platform, is licensed to institutional investment firms, banks, brokerages, application developers and academics.
API and Historical File Testing:
Based on more than a dozen published papers in leading academic journals, along with work from our own internal quantitative research team, we are so sure that you’ll find our data sets are more accurate than traditional Wall Street estimates data sets, and produce more alpha, that we make our live API and historical testing files available, at no cost, for trial. Whether you’re a systematic quantitative hedge fund, the developer of an awesome new fintech app, or an academic looking to do research, you can easily grab our historical data or live API to test before you purchase a license.
"Improving Earnings Forecasts with Estimize Crowdsourced Data"
McKinley Capital Management
One of McKinley Capital’s investment edges is its ability to analyze earnings forecast data and identify likely candidates for earnings surprises. The firm constantly searches for better and more accurate sources for its underlying data. McKinley Capital has entered a research partnership with Estimize. Estimize has surpassed some of its competitors by having developed advanced “crowd-source” technology to incorporate more information into its earnings forecasts. As reported in this paper, the firm tested the quality of Estimize estimates. The firm concluded that the use of Estimize data might provide the potential for better incorporating market-moving earnings forecast and surprise events; especially downside pre-announcement events and positive earnings surprises.
Download this paper now »
"More Accurate and Timely Estimates Lead to Better Investment Strategies"
The Wolfe Research Team, Yin Luo
Earnings estimates are the most important drivers of stock returns. Traditionally, investors collect estimates from sell-side brokerage firms via vendors such as S&P Capital IQ, Thomson Reuters (IBES), and Bloomberg. Many fundamental (e.g. discounted cash flow models) and quantitative factors (e.g. earnings yield, earnings growth, earnings revisions) rely on earnings predictions. As a result, gaining an edge with even slightly better earnings estimates can lead to tremendous alpha in the long run.
Download this paper now »
"On A Post Earnings Announcement Drift Strategy"
The Deutsche Bank Quantitative Research team
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.
Download this paper now »
"Generating Abnormal Returns Using Crowdsourced Earnings Forecasts from Estimize"
Leigh Drogen & Vinesh Jha, Former Head of Starmine Quant Research
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.
Download this paper now »
"On More Accurate Estimates And True Market Expectations"
University of San Diego, Biljana Abebambo & Barbara Bliss
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.
Download this paper now »
"The Value of Crowdsourced Earnings Forecasts"
Rice University, Rick Johnston
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.
Download this paper now »
