One of the most important parts of building a new data set, especially in the case where you are using some very orthogonal philosophies to do it, is to have the data reviewed by credible outsiders. For the past few months the Deutsche Bank Quantitative Research team has been looking at Estimize data, and recently published a big report on their findings.
The following is their conclusion:
In conclusion we found multiple benefits to using the Estimize dataset; especially in the case of short-term applications in which accuracy is essential. Another interesting byproduct of the analysis was the power of crowdsourcing. We found that some of the value-added in the Estimize dataset was due to the “wisdom of crowds” effect as more predictions give way to greater accuracy. Moreover, the diversity of the contributors provides a greater spectrum of information which can potentially improve investment strategies based on estimates.
Below are a few other excerpts, if you would like to read the full paper please contact firstname.lastname@example.org or DBEQS.Americas@db.com. To inquire about subscribing to the Estimize API please visit www.estimize.com/api.
Our initial findings show that the more timely Estimize forecasts provide greater short-term accuracy when compared to IBES.
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.
As the Estimize coverage increases, the forecast accuracy relative to IBES also increases. EPS estimates for stocks with greater than 20 analysts covering them in Estimize are more accurate 2/3 of the time.
We further compare the EPS prediction accuracy of finance professionals with non- professionals to see if the professionals make more accurate predictions. To our surprise, the data shows that finance professionals slightly underperform non- professionals.
We can also compare the accuracy of the estimates from non-professionals to those of the combination of professionals and non-professionals. The results show that there is kind of diversification effect in that combining the two actually results in better accuracy than any of two individually.
Figure 15 and Figure 16 show the average excess return to the market for earnings surprises greater than 10% for both Estimize and IBES estimates. In both cases the more timely Estimize estimates shows bigger post announcement drift for both beats and misses.