Estimize crowdsources earnings and revenue estimates from thousands of contributors including financial professionals and non-professionals. Financial professionals include institutional and independent sell-side analysts and buy-side analysts from mutual funds, hedge funds and other institutional investors (see “Estimize’s crowd,” below).
The output of Estimize’s estimates is effectively an alternative dataset which can provide investors a way to predict earnings before the publicly scheduled announcement of company earnings. Vast amounts of research and white papers on Estimize indicate that crowdsourced earnings can be used as a predictor for future earnings.
Crowdsourcing vs. sell side?
Wolfe Research compared traditional sell-side industry standard estimates from IBES and CIQ to Estimize. Wolfe found that earnings estimates from Estimize are significantly more accurate than sell-side firms (see “Crowd beats sell side,” below). Also, estimates from certain sectors like real estate and information technology were better.
Timeliness of Earnings Estimates
Heightened regulatory changes make sell-side earnings updates latent. It’s a process for a sell-side analyst to update estimates frequently. The compliance burden makes Estimize a more dynamic estimate of future earnings. The accuracy of estimates increases as the public earnings announcement date approaches.
Pre-Earnings Announcement Drift Strategy
The Estimize Pre-Earnings Drift Strategy (PEAD) takes intraday long or short positions in the company stock two weeks prior to an earnings announcement if there is a divergence in Wall Street and Estimize estimates (see “Pre-earnings drift,” below).
Post-Earnings Drift Strategy
The Estimize Post-Earnings Drift (POST) alpha is found if a company beats or misses consensus by greater than 10% (see “Post earnings drift,” below).
Strategy & Rules
Estimize developed a factor model that easily can be integrated in many different investment strategies. The Estimize signal is a -100 to +100 score generated each day and is based on pre and post earnings drift models.
In “Tale of the tape” (below) we show a dollar-neutral portfolio constructed by going long/short the top/bottom decile of scores with a daily rebalance. The signal construction process included rigorous in-and-out of-sample testing and is based on research publicly published in early 2014. The signal shows a monotonic trend with the top and bottom decile significantly outperforming those in the middle.
The Estimize Signal model would have returned 22% annually since 2012 with a Sharpe ratio of 1.44. This compares with S&P 500 return of 12.1% and a Sharpe of approximately 0.95 during the same period. The model also had a smaller maximum drawdown than the S&P 500.
The Estimize signal can be delivered via API for systematic quantitative traders, or via daily e-mails associated with a given strategy or watchlist provided by the client.
In this research, we found that Estimize provides more accurate alternative data to the traditional sell-side earnings estimates. We suggest using Estimize to complement your traditional earnings prediction model. Do the work to prove your data science outcome is similar.
