2. Background──Wisdom of the crowd:
• A considerable amount of study has demonstrated that
the estimates made by a group of people from all kinds of
background tend to outperform those made by
professionals. This phenomenon is named the ”wisdom of
the crowd”.
• There exists behavioral bias in professionals’ market
estimation such as “herding”. Fewer professionals would
make estimates too deviate from the majority even if the
estimates are justified
• Institutional bias also exists in the sense that the
financial institutes to which the professionals belong to
may encourage over-optimistic estimates
3. • Estimize is an online community
established at 2011, aiming at providing
financial forecasts for key statistics, such
as EPS, revenue and etc.
• Study has shown that market
estimation from Estimize tend to
outperform estimates from Wall Street
professionals.
Our motivation:
Examine the “wisdom of crowd”. Check if the estimates
from Estimize are indeed more accurate than the Wall
Street estimates.
Our method:
Use the Wall street estimation as the benchmark and
compare the accuracy of the Estimize estimation with it.
Prior Study:
4. Aspects to look at:
Features of the dataset
Accuracy of the estimates
Earnings surprise
Deviation from the benchmark
6. Seasonality in Estimize data:
Significant seasonality shows in the number of tickers
covered by Estimize estimation.
7. Compared to the version in the prior study, the
seasonality appears to be more significant and the
oscillation is larger.
8. Number of estimates as a function of the days
before report:
For Estimize, the number of estimates increases as
the days before report release decreases, meaning
that there are significantly more trading signals as the
time approaching the date of report release.
10. Accuracy Examination:
EPS revenue
n
% more
accurate
Estimize
error
Wall
street
error n
% more
accurate
Estimize
error
Wall
street
error
>=1
analyst 5251 51.20% 37.80% 49.50% 5691 48.10% 13% 14.70%
>=3
analysts 2734 52.30% 38.30% 57.30% 2979 48.50% 12.80% 15%
>=10
analysts 839 51.10% 24.70% 41% 918 48.90% 5.34% 4.68%
>=20
anaysts 248 53.20% 27.40% 28.80% 288 53.50% 6.40% 5.45%
11. Sector More accurate
Information Technology 60.50%
Consumer Staples 62.60%
Telecommunication Services 52.90%
Utilities 46%
Industrials 58%
Materials 51.80%
Consumer Discretionary 60.90%
Financials 57.50%
Health Care 59.50%
Energy 51.70%
Accuracy of Estimize – Sorted by Sector:
13. 0 1 1 2 2 n ndaily return X X X residual return
Some Concepts:
Earnings surprise:
(actual EPS – estimated EPS)/ estimated EPS
Residual return:
Size: total assets Value: P/E
Growth: revenue growth last year Leverage: D/E
Momentum: trailing 1 year return Yield: Dividend yield
Industry: dummy variable
Volatility: Standard deviation of trailing 1 year daily returns
14. Larger residual returns will be
generated for the trading days after
the earnings surprise, if the
estimates are more accurate
Rationale:
15. Despite the impact of incomplete data resulted from
defunct stock tickers and missing historical values,
estimates from Estimize still beat the those from Wall
street when considering the earnings surprise effect .
17. Delta ─ A measure of deviation
Definition: Percent discrepancy between the Estimize
estimation and the Wall Street estimation in the days
leading up to the report date.
Rationale:
Institutional investors trade on
numbers provided by the sell-
side. Estimize delta should
provide an early indication of
such tradings. We look at the
cumulative daily residual return
after a 10% or larger delta.
18. The cumulative event returns as a function of the
trading days after a significantly large delta ---- the
predictive power of Estimize estimation is well
demonstrated from this angle.
20. • The time span we use may be different from
the exact time period that the prior study has
used.
•Defunct tickers and missing historical data may
jeopardize the integrity of the input.
• The vagueness in the regression method used
in the initial study brings challenge to our
replication.
Potential Sources of Discrepancy
21. In spite of all the possible sources of
discrepancy, the “wisdom of crowd”
effect is still pretty significant !
Thank you for listening !
Conclusion
