Crowdsourced
Earnings
Estimates
Vinesh Jha
CQA - 24 April 2014
Agenda
•
•
•
•
•
Background: crowdsourcing financial forecasts
Data
Accuracy of a crowdsourced consensus
Returns analysis
Future directions
2
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
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
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
Agenda
•
•
•
•
•
Background: crowdsourcing financial forecasts
Data
Accuracy of a crowdsourced consensus
Returns analysis
Future directions
6
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
Coverage
8
Seasonality
9
Agenda
•
•
•
•
•
Background: crowdsourcing financial forecasts
Data
Accuracy of a crowdsourced consensus
Returns analysis
Future directions
10
More accurate
For what % of EPS reports is the Estimize consensus closer to actual
EPS than is the sell side?
>= 1 analyst
>= 3 analysts
>= 10 analysts
>= 20 analysts
n
% more
accurate
Estimize Wall Street
error
error
8971
53%
17.3%
17.4%
4916
58%
13.7%
14.5%
1438
62%
11.7%
12.6%
487
62%
12.6%
13.3%
11
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
What makes for an accurate
estimate?
N
19,796
Factor
Parameter
Track record
0.09
Difficulty
(0.04)
Coverage
0.03
Days to report
(0.10)
Bias
0.15
T
10.90
(3.95)
5.30
(12.58)
25.85
p
<.0001
<.0001
<.0001
<.0001
<.0001
13
Agenda
•
•
•
•
•
Background: crowdsourcing financial forecasts
Data
Accuracy of a crowdsourced consensus
Returns analysis
Future directions
14
After earnings
Estimize
N
IC
4614
Mean return All surprises
4548
> 1% surprises
4059
> 5% surprises
2521
> 10% surprises
1654
1 day
0.010
0.14%
0.14%
0.20%
0.20%
2 day
0.016
0.14%
0.13%
0.20%
0.25%
5 day
0.024
0.19%
0.16%
0.21%
0.27%
Wall Street
N
4614
4417
4107
2755
1849
1 day
(0.018)
0.08%
0.07%
0.13%
0.10%
2 day
(0.012)
0.03%
0.02%
0.06%
0.05%
5 day
(0.001)
0.00%
-0.01%
0.01%
-0.09%
15
After earnings (2)
Holding period
1 day
5 day
Ann ret
25.7%
10.7%
Ann SD
19.8%
14.5%
Sharpe
1.30
0.73
% days invested
29%
77%
16
Before earnings
• Estimize Delta = % diff between Estimize and Wall St consensus
• Delta predicts earnings surprises
17
Before earnings (2)
18
Before earnings (3)
Ann ret
Ann SD
Sharpe
% days invested
21.0%
5.8%
3.61
96%
19
Agenda
•
•
•
•
•
Background: crowdsourcing financial forecasts
Data
Accuracy of a crowdsourced consensus
Returns analysis
Future directions
20
Improve forecast accuracy
• Earlier contributions during the quarter
• Forecasts farther out than one quarter
• Leverage biographical data, estimate commentary, historical
surprise
21
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
Thanks!
vinesh@estimize.com
23