Crowdsourced Earnings Estimates

Vinesh Jha

CQA - 24 April 2014

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

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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

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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

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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

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

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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

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Coverage

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Seasonality

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

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More accurateFor what % of EPS reports is the Estimize consensus closer to actual EPS than is the sell side?

n% more

accurateEstimize

error Wall Street

error

>= 1 analyst 8971 53% 17.3% 17.4%

>= 3 analysts 4916 58% 13.7% 14.5%

>= 10 analysts 1438 62% 11.7% 12.6%

>= 20 analysts 487 62% 12.6% 13.3%

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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

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What makes for an accurate estimate?

N 19,796

Factor Parameter T pTrack record 0.09 10.90 <.0001Diffi culty (0.04) (3.95) <.0001Coverage 0.03 5.30 <.0001Days to report (0.10) (12.58) <.0001Bias 0.15 25.85 <.0001

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

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After earnings

Estimize Wall StreetN 1 day 2 day 5 day N 1 day 2 day 5 day

IC 4614 0.010 0.016 0.024 4614 (0.018) (0.012) (0.001) Mean return All surprises 4548 0.14% 0.14% 0.19% 4417 0.08% 0.03% 0.00%

> 1% surprises 4059 0.14% 0.13% 0.16% 4107 0.07% 0.02% -0.01%> 5% surprises 2521 0.20% 0.20% 0.21% 2755 0.13% 0.06% 0.01%> 10% surprises 1654 0.20% 0.25% 0.27% 1849 0.10% 0.05% -0.09%

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After earnings (2)

Holding period1 day 5 day

Ann ret 25.7% 10.7%Ann SD 19.8% 14.5%Sharpe 1.30 0.73 % days invested 29% 77%

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Before earnings• Estimize Delta = % diff between Estimize and Wall St

consensus• Delta predicts earnings surprises

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Before earnings (2)

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Before earnings (3)

Ann ret 21.0%Ann SD 5.8%Sharpe 3.61 % days invested 96%

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

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

historical surprise

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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

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Thanks!

vinesh@estimize.com