This document summarizes a re-examination of crowd-sourced earnings forecasts from Estimize. It finds that:
1) Estimize estimates tend to be more accurate than Wall Street estimates, especially for sectors like information technology, consumer staples, and consumer discretionary. Estimize accuracy increases with more analyst estimates.
2) Estimize estimates better predict earnings surprises, generating larger returns after earnings surprises.
3) Estimize estimates deviate more from Wall Street benchmarks as the report date approaches, providing an early indication of institutional investor trading. Large deviations in Estimize estimates predict positive cumulative returns after the report date.
4) Despite potential data issues, the "wisdom of the crowd" effect from
Presentation on "A PREDICTIVE ANALYTICS PRIMER" made as a task for the internship on "DATA ANALYTICS WITH MANAGERIAL APPLICATIONS" under Professor Sameer Mathur, IIM Lucknow. Submitted by TARANG JAIN,DTU
Special Report - Is the OPEC Agreement a Game Changer?Amir Khan
Contrary to expectations, OPEC managed to reach an agreement at the sidelines of the Global Energy Forum held in Algiers. But it's too early to say this will be turning for the oil market.
Presentation on "A PREDICTIVE ANALYTICS PRIMER" made as a task for the internship on "DATA ANALYTICS WITH MANAGERIAL APPLICATIONS" under Professor Sameer Mathur, IIM Lucknow. Submitted by TARANG JAIN,DTU
Special Report - Is the OPEC Agreement a Game Changer?Amir Khan
Contrary to expectations, OPEC managed to reach an agreement at the sidelines of the Global Energy Forum held in Algiers. But it's too early to say this will be turning for the oil market.
En el tiempo del Antiguo Testamento, era un requisito, compensar el daño hecho, y agregar una multa (Núm. 5:7). Aunque no vivimos bajo esta ley, el principio de la restitución es válido hoy._
o cualquier cosa acerca de la cual juró falsamente; hará completa restitución de ello y le añadirá una quinta parte más. Se la dará al que le pertenece el día que presente su ofrenda por la culpa. Levítico 6:5
"Fundamental Forecasts: Methods and Timing" by Vinesh Jha, CEO of ExtractAlphaQuantopian
From QuantCon 2017:
Fundamental and quantitative stock selection research has long focused on creating accurate forecasts of company fundamentals such as earnings and revenues. In this talk we examine why fundamental forecasts are powerful and survey some classic methods for generating these forecasts. Next we explore some newer methodologies which can be effective in generating more accurate fundamental forecasts, including new uses of traditional data as well as novel crowdsourced and online behavior databases. Finally, we present new research examining the temporal variation in efficacy of these forecasts with an eye towards understanding the market conditions in which an accurate fundamental forecast can be more or less profitable.
The talk has three parts : the first part gives an overview of data science work, including roadmap of data science team, responsibility and value of data scientists; the second part talks about pitfalls in analysis and teaches some common analysis methods; the third part takes decision support, metrics and AB testing as examples to explain the data science work and how they are translated to business value.
Analysis on Email Marketing Campaigns and Analytics to improve business decisions
You can find analysis and code to it here: https://pradeep.code.blog/2020/05/29/email-marketing-efficacy-and-business-decisions/
Smarter Beta - S&P Global Market IntelligenceSymphony.com
S&P Global Market Intelligence will discuss how to derive smart beta from global alpha sources and review alternative implementations of the portfolio construction process.
FiBAN's business angel training "Business Angel Returns" by Robert Wiltbank -...FiBAN
Presention shared by Dr. Robert Wiltbank at FiBAN's business angel training in Helsinki, 3rd of November.
All the presentations and videos are gathered here: https://www.fiban.org/robertwiltbank
Presentations given:
1. Comparison of Finnish and US angel activity
https://www.youtube.com/watch?v=UKdmr...
- Slides:
2. Angel Returns: https://www.youtube.com/watch?v=juuAK...
- Slides:
3. Effective business angel strategies: https://www.youtube.com/watch?v=TsZQd...
- Slides:
4. Effectuation in Venture investing - Do experts make decisions differently?: https://www.youtube.com/watch?v=miWap...
- Slides
For additional details and questions: https://www.fiban.org/robertwiltbank
Mercer Capital's Value Focus: FinTech Industry | Second Quarter 2015Mercer Capital
Mercer Capital’s quarterly newsletter, FinTech Watch, provides an overview of the FinTech industry, including public market performance, valuation multiples for public FinTech companies, and articles of interest from around the web. This newsletter focuses on FinTech segments, including payment processors, technology, and solutions companies, examining general economic and industry trends as well as a summary of M&A and venture capital activity.
En el tiempo del Antiguo Testamento, era un requisito, compensar el daño hecho, y agregar una multa (Núm. 5:7). Aunque no vivimos bajo esta ley, el principio de la restitución es válido hoy._
o cualquier cosa acerca de la cual juró falsamente; hará completa restitución de ello y le añadirá una quinta parte más. Se la dará al que le pertenece el día que presente su ofrenda por la culpa. Levítico 6:5
"Fundamental Forecasts: Methods and Timing" by Vinesh Jha, CEO of ExtractAlphaQuantopian
From QuantCon 2017:
Fundamental and quantitative stock selection research has long focused on creating accurate forecasts of company fundamentals such as earnings and revenues. In this talk we examine why fundamental forecasts are powerful and survey some classic methods for generating these forecasts. Next we explore some newer methodologies which can be effective in generating more accurate fundamental forecasts, including new uses of traditional data as well as novel crowdsourced and online behavior databases. Finally, we present new research examining the temporal variation in efficacy of these forecasts with an eye towards understanding the market conditions in which an accurate fundamental forecast can be more or less profitable.
The talk has three parts : the first part gives an overview of data science work, including roadmap of data science team, responsibility and value of data scientists; the second part talks about pitfalls in analysis and teaches some common analysis methods; the third part takes decision support, metrics and AB testing as examples to explain the data science work and how they are translated to business value.
Analysis on Email Marketing Campaigns and Analytics to improve business decisions
You can find analysis and code to it here: https://pradeep.code.blog/2020/05/29/email-marketing-efficacy-and-business-decisions/
Smarter Beta - S&P Global Market IntelligenceSymphony.com
S&P Global Market Intelligence will discuss how to derive smart beta from global alpha sources and review alternative implementations of the portfolio construction process.
FiBAN's business angel training "Business Angel Returns" by Robert Wiltbank -...FiBAN
Presention shared by Dr. Robert Wiltbank at FiBAN's business angel training in Helsinki, 3rd of November.
All the presentations and videos are gathered here: https://www.fiban.org/robertwiltbank
Presentations given:
1. Comparison of Finnish and US angel activity
https://www.youtube.com/watch?v=UKdmr...
- Slides:
2. Angel Returns: https://www.youtube.com/watch?v=juuAK...
- Slides:
3. Effective business angel strategies: https://www.youtube.com/watch?v=TsZQd...
- Slides:
4. Effectuation in Venture investing - Do experts make decisions differently?: https://www.youtube.com/watch?v=miWap...
- Slides
For additional details and questions: https://www.fiban.org/robertwiltbank
Mercer Capital's Value Focus: FinTech Industry | Second Quarter 2015Mercer Capital
Mercer Capital’s quarterly newsletter, FinTech Watch, provides an overview of the FinTech industry, including public market performance, valuation multiples for public FinTech companies, and articles of interest from around the web. This newsletter focuses on FinTech segments, including payment processors, technology, and solutions companies, examining general economic and industry trends as well as a summary of M&A and venture capital activity.
Will your portfolio deliver the growth you expect? Now, for the first time, be able to accurately predict which projects in your portfolio will fail - and why. A new, proven method for accurate prediction of future success will improve what your portfolio can deliver.
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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