1. Crowdsourced Earnings Estimates Vinesh Jha CQA - 24 April 2014

2. Agenda Background: crowdsourcing financial forecasts Data Accuracy of a crowdsourced consensus Returns analysis Future directions 2

3. Forecasting Crowdsourced forecasts have mostly focused on stock price performance (e.g., Motley Fool CAPS) or the outcomes of economic events (e.g., prediction markets) –There are a lot of moving parts in stock prices By focusing on EPS forecasts, we can isolate a particular aspect of forecasting skill Replaces phone calls and buy side huddles And we have a ready-made benchmark in the form of sell side estimates –Sell side biases are well documented. Herding, banking, risk aversion Hope is that crowdsourced forecasts better represent the market’s expectations Improve valuation, revisions and surprise models, research 3

4. Estimize Founded in 2011 by Leigh Drogen Platform is free and open for contributors and consumers Pseudonymous Contributor base –Buy side, independent, individuals, and students –Diversity of backgrounds and forecasting methodologies –Users can contribute biographical information 4

5. Estimize 25,000 registered users 75,000 unique viewers of data last quarter 4,000 contributors 17,000 estimates made last quarter Coverage (3+ estimates) on 900+ stocks last quarter 5

6. Agenda Background: crowdsourcing financial forecasts Data Accuracy of a crowdsourced consensus Returns analysis Future directions 6

7. Data US listed stocks, Nov 2011 – Mar 2014 Universe, updated monthly –# Estimize contributors ≥ 3 –Market cap ≥ $100mm –ADV ≥ $1mm –Price ≥ $4 Potentially erroneous estimates flagged for review or removal In sample analysis restricted to quarters reporting during 2012 Returns residualized to industry, yield, volatility, momentum, size, value, growth, leverage 7

8. Coverage 8

9. Seasonality 9

10. Agenda Background: crowdsourcing financial forecasts Data Accuracy of a crowdsourced consensus Returns analysis Future directions 10

11. More accurate For what % of EPS reports is the Estimize consensus closer to actual EPS than is the sell side? 11

12. What makes for an accurate estimate? Regress estimate-level accuracy (% error) against –Track record + how good has the analyst been in this sector in the past? accuracy is persistent: better forecasters remain better –Difficulty of forecasting - condition track record on the overall accuracy of the Estimize community Expect less accuracy if everyone’s been inaccurate –Amount of coverage + more is better, to a point –Days to report - more recent forecasts contain more information –Bias + higher estimates tend to be more accurate 12

13. What makes for an accurate estimate? 13

14. Agenda Background: crowdsourcing financial forecasts Data Accuracy of a crowdsourced consensus Returns analysis Future directions 14

15. After earnings 15

16. After earnings (2) 16

17. Before earnings Estimize Delta = % diff between Estimize and Wall St consensus Delta predicts earnings surprises 17

18. Before earnings (2) 18

19. Before earnings (3) 19

20. Agenda Background: crowdsourcing financial forecasts Data Accuracy of a crowdsourced consensus Returns analysis Future directions 20

21. Improve forecast accuracy Earlier contributions during the quarter Forecasts farther out than one quarter Leverage biographical data, estimate commentary, historical surprise 21

22. Forecast more things Mergers & acquisitions (www.mergerize.com) Macroeconomics Growth & valuation Industry aggregates Industry specific (same store sales, iPods/iPads, FDA approvals, etc) Other structured data 22

23. Thanks! vinesh@estimize.com 23