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Crowdsourced Earnings Estimates

Crowdsourced Earnings Estimates. Vinesh Jha CQA - 24 April 2014. Agenda. Background: crowdsourcing financial forecasts Data Accuracy of a crowdsourced consensus Returns analysis Future directions. Forecasting.

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Crowdsourced Earnings Estimates

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  1. CrowdsourcedEarnings Estimates Vinesh Jha CQA - 24 April 2014

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

  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

  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

  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

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

  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

  8. Coverage

  9. Seasonality

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

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

  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 Estimizecommunity • 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

  13. What makes for an accurate estimate?

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

  15. After earnings

  16. After earnings (2)

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

  18. Before earnings (2)

  19. Before earnings (3)

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

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

  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

  23. Thanks! vinesh@estimize.com

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