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Crowdsourced Earnings Estimates

Explore how crowdsourced earnings estimates can improve financial forecasting accuracy and returns analysis, focusing on EPS forecasts to isolate forecasting skill and benchmark sell-side estimates. Learn about Estimize platform founded by Leigh Drogen in 2011, offering free and open access for contributors with a diverse background. Discover the analysis of US listed stocks from Nov 2011 to Mar 2014, providing insights on estimate accuracy and coverage seasonality. Find out how the Estimize consensus better predicts actual EPS than sell-side estimates and what factors contribute to an accurate estimate. Delve into the impact of biases, coverage, days to report, and analyst track records on forecast accuracy, as well as how Estimize Delta predicts earnings surprises. Explore methods to enhance forecast accuracy through earlier contributions, leveraging biographical data, and forecasting a wider range of events, from mergers to macroeconomics. Contact vinesh@estimize.com for more information.

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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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