Alternative Data Integration, Analysis and Investment Research
Yin Luo, CFA Vice Chairman Quantitative Research, Economics, and Portfolio Strategy
Alternative Data Integration, Analysis and Investment Research
QES Desk Phone: 1.646.582.9230 [email protected]
L2Q Conference
June 20, 2017
DO NOT FORWARD – DO NOT DISTRIBUTE – DOCUMENT CAN ONLY BE PRINTED TWICE This report is limited solely for the use of clients of Wolfe Research. Please refer to the DISCLOSURE SECTION located at the end of this report for Analyst Certifications and Other Disclosures. For Important Disclosures, please go to www.WolfeResearch.com/Disclosures or write to us at Wolfe Research, LLC, 420 Lexington Avenue, Suite 648, New York, NY 10170
Agenda Table of Contents 1. Introducing Luo’s QES Research 2. Crowdsourcing Revenue and Earnings Estimates 3. Text Mining Unstructured Corporate Filing Data
2
1. Introducing Luo’s QES Research •
•
•
•
•
Top Ranked Quantitative and Macro Research Team. The team has been ranked #1 in the Institutional Investor’s II-All America, II-Europe, and II-Asia surveys in the Quantitative Research sector, and top ranked in the Portfolio Strategy and Accounting & Tax Policy categories. Big Data and Machine Learning. We fully incorporate Big Data (e.g., text mining, news sentiment, satellite imagery, securities lending, crowding sourcing) and machine learning in our research, as reflected in our LEAP global stock selection model. Systematic Global Macro Research. Our research on Nowcasting economic growth in >40 countries/regions has received tremendous feedback from clients. Style rotation and factor timing is a core component of our global stock selection models. Alternative data sources (e.g., news sentiment, real-time hiring, satellite imagery, Google trends) are also fully integrated in our macro research. Useful Tools for Fundamental Managers. In addition to research, we also provide a suite of useful tools, including online screening and factor performance tracking, industry-specific modeling, position sizing and portfolio construction, and portfolio analytics to discretionary managers. Please contact us at [email protected], if you are interested in our research and services. 3
The Complex Web of Data
Sources: Wolfe Research Luo’s QES
4
2. Crowdsourcing Earnings and Revenue Estimates •
•
•
•
Alternative Big Data on Earnings and Revenue Estimate. In this research, we study an alternative data source based on the concept of crowdsourcing. Estimize is an online platform that allows individuals with different background to contribute their financial forecast. We find Estimize estimates to be not only more accurate and timelier than the sell-side, but also highly complementary to traditional factors. Estimize FES Model. Diving into detailed Estimize estimates, we find that accuracy can be further improved along three dimensions: the freshness of the estimates, analyst experience, and analyst skill. We then introduce a smart Estimize consensus called FES (Freshness, Experience, and Skill). Smart Strategies around Earnings Announcement. We explore three different type of trading strategies around earnings releases using the Estimize data. The pre-earnings announcement strategy buys stocks based on earnings revisions in the week before the earnings reporting date. PEAD (Post Earning Announcement Drift) strategy attempts to capture the drift alpha immediately after the earnings announcement, based on earnings surprise. Lastly, we propose a low risk longonly strategy by avoiding earnings risk and earnings uncertainty. Enhanced Value Strategies. Many fundamental and quantitative strategies explicitly or implicitly rely on earnings and revenue estimates. For long-term value investors, we show how Estimize data and our FES model can be used to boost performance. In the end, we also overlay our enhanced value strategy with a low risk tilt (by avoiding earnings uncertainty) to further improve return and reduce risk. 5
a) The Basics of Crowdsourcing •
Earnings and revenue estimates are probably the most important drivers of stock returns and risks. Unlike conventional sell-side consensus, Estimize crowdsources estimates from a wide range of contributors.
Breakdown of Estimize data Finance Professionals
Non-professionals
Contributors to the Estimize database Telecommunication Services Utilities Energy Consumer Discretionary
46%
54%
Consumer Staples Materials
Non Professional
Industrials Financials Information Technology Health Care Student Academia Insurance Firm Wealth Manager
Sell Side
Investment Bank
Breakdown of finance professional Independent
Buy Side
Sell Side
Financial Advisor Broker Other Endowment Fund Pension Fund Private Equity
45%
30%
Fund of Funds
Financial Professional
Buy Side
Mutual Fund Proprietary Trading Firm
25%
Venture Capital Hedge Fund Asset Manager
Independent
Sources: Estimize, Wolfe Research Luo’s QES
Other Independent Research Other
6
Estimize Data Distribution # of companies (in the Russell 3000 index) with Estimize analyst coverage 1600
Number of analysts
>=1 anlalyst >=3 anlalysts
1200
>=10 anlalysts >=30 anlalysts
800
400
0
Estimize data sector distribution
Number of days before earnings report
25% 20% 15%
10% 5% 0%
% of stocks in the Estimize database
% of stocks in the Russell 3000 index
Sources: Estimize, S&P Capital IQ, FTSE Russell, Wolfe Research Luo’s QES
7
b) The Accuracy of Crowdsourced Estimates EPS estimate accuracy
Revenue estimate accuracy
100%
100%
90%
90% 80%
80% 70%
57%
59%
60%
62%
64%
70%
60%
60%
50%
50%
40%
40%
30%
20%
30% 43%
41%
40%
38%
36%
10%
20%
48%
49%
50%
51%
51%
52%
51%
50%
49%
49%
>=1 analyst
>=3 analysts
>=5 analysts
>=10 analysts
>=30 analysts
10%
0%
>=1 analyst
>=3 analysts
>=5 analysts
Sell-side more accurate
>=10 analysts
>=30 analysts
0%
Estimize more accurate
Estimize EPS accuracy by sector
Sell-side more accurate
Estimize more accurate
Estimize accuracy, domestic versus multinational firms 100%
100%
90% 80%
70%
59.1%
60.6%
60.8%
61.4%
40.9%
39.4%
39.2%
38.6%
>20% exUS
>50% exUS
60%
50%
50% 40% 30%
20%
0%
10% 0% 10% exUS Sellside more accurate
Sellside more accurate
Estimize more accurate
Estimize more accurate
Sources: Estimize, IBES, S&P Capital IQ, FTSE Russell, Wolfe Research Luo’s QES
8
c) Factors Determining Analyst Accuracy Comparing the above and below median freshness Comparing above and below median experience Comparing above and below median skill Older estimates, more accurate
Below median experience analysts, more accurate
Below median skill analysts, more accurate
Newer estimates, more accurate
Above median experience analysts, more accurate
Above median skill analysts, more accurate
38%
39% 61%
59%
62%
Comparing top and bottom decile freshness
41%
Comparing top and bottom decile experience
Comparing top and bottom decile skill
Oldest estimate, more accurate
Bottom decile experience analysts, more accurate
Bottom decile skill analysts, more accurate
Newest estimates, more accurate
Top decile experience analysts, more accurate
Top decile skill analysts, more accurate
35%
34%
66%
Sources: Estimize, IBES, S&P Capital IQ, FTSE Russell, Wolfe Research Luo’s QES
65%
38%
62%
9
Estimize FES (Freshness, Experience, and Skill) Model Weighting the estimate by the freshness, analyst experience, and analyst skill Equal weight, more accurate
Equal weight, more accurate
Equal weight, more accurate
Weighted by freshness, more accurate
Weighted by experience, more accurate
Weighted by skill, more accurate
46% 54%
53%
47%
47%
53%
Estimize FED model versus single weighting scheme FES model, more accurate
FES model, more accurate
FES model, more accurate
Weighted by freshness, more accurate
Weighted by experience, more accurate
Weighted by skill, more accurate
48%
52%
46%
Sources: Estimize, IBES, S&P Capital IQ, FTSE Russell, Wolfe Research Luo’s QES
45% 54%
55%
10
d) Trading around Earnings Announcements •
•
•
•
Earnings announcement is among the most significant market moving corporate events As earnings release date approaches, the Estimate data becomes more and more accurate. Estimize provides timelier estimates, while sell-side analysts are better at predicting earnings over a longer horizon. If the consensus moves up right before earnings announcement, a company is more likely to beat the consensus.
Estimize EPS accuracy as a function of the # of days prior to earnings announcement Sell-side more accurate
50%
0% 0
1
IBES EPS
Estimize EPS
Actual EPS
2
3
4
5
6
7
8 9 10 11 12 13 # of days prior to earnings release
CIQ
Price
0.06
17.0
0.05
16.0
14
15
16
17
18
19
20
# of estimates for Pandora Media before earnings announcement
An example of Pandora Media before earnings announcement CIQ EPS
Estimize more accurate
100%
IBES
Estimize
60
0.03
14.0
0.02
13.0
0.01
12.0
dates
Sources: Estimize, IBES, S&P Capital IQ, FTSE Russell, Wolfe Research Luo’s QES
# of estimates
15.0
EPS
0.04
Stock price
50 40 30 20 10 0
dates
11
Earnings revisions prior to the announcement date leads announcement day return 𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠𝑅𝑒𝑣𝑖𝑠𝑖𝑜𝑛,,. =
𝐸𝑃𝑆,,. − 𝐸𝑃𝑆,,.34
𝐸𝑁𝑅𝑃,,. =
𝐸𝑃𝑆,,.34
𝐸𝑃𝑆,,. − 𝐸𝑃𝑆,,.34 𝑃𝑟𝑖𝑐𝑒,,.
Percentage of EPS changes in the week before earnings announcement
A) Most positive EPS revisions
B) Most negative EPS revisions
120%
0%
100%
-10% -20%
80%
-30%
60%
-40%
40%
-50% -60%
20%
-70%
0%
-80%
Sell-side top 1%
top 2%
Estimize FES top 5%
top 10%
Sell-side
bottom 1%
bottom 2%
Estimize FES
bottom 5%
bottom 10%
Average excess earning announcement day return
Sources: Estimize, IBES, S&P Capital IQ, FTSE Russell, Wolfe Research Luo’s QES
12
Post Earnings Announcement Drift (PEAD) •
PEAD is more significant, if a company beats (or misses) the Estimize estimates. PEAD decays away after two days.
Positive earnings surprise, first day PEAD
Negative earnings surprise, first day PEAD
0.6%
0.0%
0.5%
-0.1% -0.2%
0.4%
-0.3%
0.3%
-0.4%
0.2%
-0.5%
0.1%
-0.6% -0.7%
0.0% 10% surprise
20% surprise Estimize
30% surprise
40% surprise
10% surprise
20% surprise Estimize
Sell-side
30% surprise
40% surprise
Sell-side
Excess return when EPS misses over 40%
Excess return when EPS beats over 40%
0.2%
0.8%
0.0%
0.6%
-0.2%
0.4%
-0.4%
0.2%
-0.6%
0.0%
-0.8%
-0.2% day 1
day 2 Estimize
day 3
Sell-side
Sources: Estimize, IBES, S&P Capital IQ, FTSE Russell, Wolfe Research Luo’s QES
day 1
day 2 Estimize
day 3
Sell-side
13
e) Long-term Investment Strategies •
•
•
Benchmark earnings yield. For the benchmark factor, we use the consensus sell-side EPS, by taking a simple average of CIQ and IBES. Standard Estimize earnings yield. In this case, we replace the sell-side consensus with the Estimize estimates when we have at least three contributors in the Estimize database. Estimize FES earnings yield. Instead of replacing with the standard Estimize estimates, we use the FES model introduced in the previous section.
𝐹𝑄1 𝐸𝑃𝑆 𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠𝑌𝑖𝑒𝑙𝑑 = 𝑃𝑟𝑖𝑐𝑒
Cumulative performance, long/short quintile portfolio on S&P500 1.3
1.2
1.1
1.0
0.9
Benchmark earnings yield
Estimize Avg earnings yield
Estimize FES earnings yield
Monthly turnover
Sharpe ratio, different transaction cost assumptions
100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%
0.5 0.4 0.3 0.2 0.1
0.0 No cost Benchmark earnings yield
Estimize Avg earnings yield
Estimize FES earnings yield
Sources: Estimize, IBES, S&P Capital IQ, FTSE Russell, Wolfe Research Luo’s QES
2 bps
Benchmark earnings yield Estimize FES earnings yield
5 bps
Estimize Avg earnings yield
14
Enhanced Value Strategies • •
Enhanced earnings yield using Estimize data performs equally well in both large- and small-cap universes. Enhanced value strategies produce decent performance in a long-only context.
Sharpe ratio, different universe
Sharpe ratio, monthly versus daily rebalance 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0
1.4
1.2 1.0 0.8 0.6 0.4
0.2
Benchmark earnings yield
0.0
S&P 500
Russell 3000 Baseline
Russell 3000 sector neutral
3.0
Estimize FES earnings yield
Russell 3000 universe, monthly rebalance
Estimize
Long only portfolio performance, Russell 3000 universe
Estimize Avg earnings yield
Russell 3000 universe, daily rebalance
Sharpe Ratio, Long only portfolio Russell 3000 universe 1.4
2.5
1.2
2.0
1.0
1.5
0.8
1.0
0.6
0.5
0.4
0.0
0.2 Equally weighted Russell 3000
Top quintile Estimize earning yield
Sources: Estimize, IBES, S&P Capital IQ, FTSE Russell, Wolfe Research Luo’s QES
0.0 Equally weighted Russell 3000
Top quintile Estimize earning yield
15
4. Text Mining Unstructured Corporate Filing Data # of EDGAR Filings (Daily)
Average # of Words in the 10-K Filings
16 Sources: EDGAR, Wolfe Research Luo’s QES
Lazy Prizes
•
•
Firms in general use a well-defined format for their annual and quarterly filings. Almost identical language is simply repeated year after year, until someone actively intervenes and makes changes. When firms do break away from their tradition of textual descriptions in their filings, they typically foresee significant changes in their business, risk, or corporate strategy. Our NLP algorithms are able to identify potential issues from textual information among US transportation companies.
US Transportation Industry, Cumulative Performance
An Example: American Express 98 88
78
Price
•
Substantial and increasingly intense competition for partner relationships; risk surrounding Airline industry.
68 58 48
Ongoing legal proceedings can cause substantial monetary and reputational damages.
38
US Transportation Industry, Returns by Merciless
Sources: Bloomberg Finance LLP, EDGAR, FTSE Russell, S&P Capital IQ, Thomson Reuters, Wolfe Research Luo’s QES
17
Systematic Profiling EDGAR Composite (SPEC) Model SPEC model rank IC
Quintile portfolio returns
Long/short monthly turnover
SPEC model Long/short portfolio performance
Portfolio Sharpe ratio
Rank IC decay
Sources: Bloomberg Finance LLP, FTSE Russell, S&P Capital IQ, Thomson Reuters, Wolfe Research Luo’s QES
18
Interaction with traditional factors
•
•
•
•
•
Factor correlations with EDGAR composite models
Earnings Yield Earnings Revision Momentum 1M Reversal ROE EPS growth Market cap Volatility Composite 10K 11% 6% 4% 1% 10% -3% 11% -14% Composite 10Q 8% 3% 3% 1% 7% -2% 8% -10% Composite All 11% 5% 5% 2% 11% -2% 11% -13% Annualized returns of Style composites vs base factors 9 8 7
CAGR (%)
•
Lastly, it is interesting to note that our factor has almost no exposure to classic risk factors such as size, beta or volatility. One of the most important reasons of relying on alternative Big Data sources rests on the diversification benefit, in that they are expected to have minimal correlation with traditional factors. As expected, our signals based on EDGAR text mining are almost uncorrelated to any of our traditional factors. Finally, as a unique alpha source, the SPEC model should complement and add value to traditional factors. The performance of all traditional factors improves remarkably, when the SPEC is added. Growth factors witness the largest improvement. Improvement in risk-adjusted performance is even more significant. The Sharpe ratio improves by 50% for the majority of the conventional factors.
6 5
4 3 2 1 0 Earnings Revision
Earnings Revision composite
Earnings Yield
Earnings Yield composite
EPS growth EPS growth Momentum Momentum composite composite
ROE
ROE composite
ROE
ROE composite
Sharpe ratio of Style composites vs base factors 0.7 0.6
Sharpe ratio
•
0.5 0.4
0.3 0.2
0.1 0 Earnings Revision
Earnings Revision composite
Earnings Yield
Sources: Bloomberg Finance LLP, FTSE Russell, S&P Capital IQ, Thomson Reuters, Wolfe Research Luo’s QES
Earnings EPS growth EPS growth Momentum Momentum Yield composite composite composite
19
An Interactive Web Portal
20
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21
Alternative Data Integration, Analysis and Investment Research
QES Desk Phone: 1.646.582.9230 [email protected]
L2Q Conference
June 20, 2017
DO NOT FORWARD – DO NOT DISTRIBUTE – DOCUMENT CAN ONLY BE PRINTED TWICE This report is limited solely for the use of clients of Wolfe Research. Please refer to the DISCLOSURE SECTION located at the end of this report for Analyst Certifications and Other Disclosures. For Important Disclosures, please go to www.WolfeResearch.com/Disclosures or write to us at Wolfe Research, LLC, 420 Lexington Avenue, Suite 648, New York, NY 10170
Agenda Table of Contents 1. Introducing Luo’s QES Research 2. Crowdsourcing Revenue and Earnings Estimates 3. Text Mining Unstructured Corporate Filing Data
2
1. Introducing Luo’s QES Research •
•
•
•
•
Top Ranked Quantitative and Macro Research Team. The team has been ranked #1 in the Institutional Investor’s II-All America, II-Europe, and II-Asia surveys in the Quantitative Research sector, and top ranked in the Portfolio Strategy and Accounting & Tax Policy categories. Big Data and Machine Learning. We fully incorporate Big Data (e.g., text mining, news sentiment, satellite imagery, securities lending, crowding sourcing) and machine learning in our research, as reflected in our LEAP global stock selection model. Systematic Global Macro Research. Our research on Nowcasting economic growth in >40 countries/regions has received tremendous feedback from clients. Style rotation and factor timing is a core component of our global stock selection models. Alternative data sources (e.g., news sentiment, real-time hiring, satellite imagery, Google trends) are also fully integrated in our macro research. Useful Tools for Fundamental Managers. In addition to research, we also provide a suite of useful tools, including online screening and factor performance tracking, industry-specific modeling, position sizing and portfolio construction, and portfolio analytics to discretionary managers. Please contact us at [email protected], if you are interested in our research and services. 3
The Complex Web of Data
Sources: Wolfe Research Luo’s QES
4
2. Crowdsourcing Earnings and Revenue Estimates •
•
•
•
Alternative Big Data on Earnings and Revenue Estimate. In this research, we study an alternative data source based on the concept of crowdsourcing. Estimize is an online platform that allows individuals with different background to contribute their financial forecast. We find Estimize estimates to be not only more accurate and timelier than the sell-side, but also highly complementary to traditional factors. Estimize FES Model. Diving into detailed Estimize estimates, we find that accuracy can be further improved along three dimensions: the freshness of the estimates, analyst experience, and analyst skill. We then introduce a smart Estimize consensus called FES (Freshness, Experience, and Skill). Smart Strategies around Earnings Announcement. We explore three different type of trading strategies around earnings releases using the Estimize data. The pre-earnings announcement strategy buys stocks based on earnings revisions in the week before the earnings reporting date. PEAD (Post Earning Announcement Drift) strategy attempts to capture the drift alpha immediately after the earnings announcement, based on earnings surprise. Lastly, we propose a low risk longonly strategy by avoiding earnings risk and earnings uncertainty. Enhanced Value Strategies. Many fundamental and quantitative strategies explicitly or implicitly rely on earnings and revenue estimates. For long-term value investors, we show how Estimize data and our FES model can be used to boost performance. In the end, we also overlay our enhanced value strategy with a low risk tilt (by avoiding earnings uncertainty) to further improve return and reduce risk. 5
a) The Basics of Crowdsourcing •
Earnings and revenue estimates are probably the most important drivers of stock returns and risks. Unlike conventional sell-side consensus, Estimize crowdsources estimates from a wide range of contributors.
Breakdown of Estimize data Finance Professionals
Non-professionals
Contributors to the Estimize database Telecommunication Services Utilities Energy Consumer Discretionary
46%
54%
Consumer Staples Materials
Non Professional
Industrials Financials Information Technology Health Care Student Academia Insurance Firm Wealth Manager
Sell Side
Investment Bank
Breakdown of finance professional Independent
Buy Side
Sell Side
Financial Advisor Broker Other Endowment Fund Pension Fund Private Equity
45%
30%
Fund of Funds
Financial Professional
Buy Side
Mutual Fund Proprietary Trading Firm
25%
Venture Capital Hedge Fund Asset Manager
Independent
Sources: Estimize, Wolfe Research Luo’s QES
Other Independent Research Other
6
Estimize Data Distribution # of companies (in the Russell 3000 index) with Estimize analyst coverage 1600
Number of analysts
>=1 anlalyst >=3 anlalysts
1200
>=10 anlalysts >=30 anlalysts
800
400
0
Estimize data sector distribution
Number of days before earnings report
25% 20% 15%
10% 5% 0%
% of stocks in the Estimize database
% of stocks in the Russell 3000 index
Sources: Estimize, S&P Capital IQ, FTSE Russell, Wolfe Research Luo’s QES
7
b) The Accuracy of Crowdsourced Estimates EPS estimate accuracy
Revenue estimate accuracy
100%
100%
90%
90% 80%
80% 70%
57%
59%
60%
62%
64%
70%
60%
60%
50%
50%
40%
40%
30%
20%
30% 43%
41%
40%
38%
36%
10%
20%
48%
49%
50%
51%
51%
52%
51%
50%
49%
49%
>=1 analyst
>=3 analysts
>=5 analysts
>=10 analysts
>=30 analysts
10%
0%
>=1 analyst
>=3 analysts
>=5 analysts
Sell-side more accurate
>=10 analysts
>=30 analysts
0%
Estimize more accurate
Estimize EPS accuracy by sector
Sell-side more accurate
Estimize more accurate
Estimize accuracy, domestic versus multinational firms 100%
100%
90% 80%
70%
59.1%
60.6%
60.8%
61.4%
40.9%
39.4%
39.2%
38.6%
>20% exUS
>50% exUS
60%
50%
50% 40% 30%
20%
0%
10% 0% 10% exUS Sellside more accurate
Sellside more accurate
Estimize more accurate
Estimize more accurate
Sources: Estimize, IBES, S&P Capital IQ, FTSE Russell, Wolfe Research Luo’s QES
8
c) Factors Determining Analyst Accuracy Comparing the above and below median freshness Comparing above and below median experience Comparing above and below median skill Older estimates, more accurate
Below median experience analysts, more accurate
Below median skill analysts, more accurate
Newer estimates, more accurate
Above median experience analysts, more accurate
Above median skill analysts, more accurate
38%
39% 61%
59%
62%
Comparing top and bottom decile freshness
41%
Comparing top and bottom decile experience
Comparing top and bottom decile skill
Oldest estimate, more accurate
Bottom decile experience analysts, more accurate
Bottom decile skill analysts, more accurate
Newest estimates, more accurate
Top decile experience analysts, more accurate
Top decile skill analysts, more accurate
35%
34%
66%
Sources: Estimize, IBES, S&P Capital IQ, FTSE Russell, Wolfe Research Luo’s QES
65%
38%
62%
9
Estimize FES (Freshness, Experience, and Skill) Model Weighting the estimate by the freshness, analyst experience, and analyst skill Equal weight, more accurate
Equal weight, more accurate
Equal weight, more accurate
Weighted by freshness, more accurate
Weighted by experience, more accurate
Weighted by skill, more accurate
46% 54%
53%
47%
47%
53%
Estimize FED model versus single weighting scheme FES model, more accurate
FES model, more accurate
FES model, more accurate
Weighted by freshness, more accurate
Weighted by experience, more accurate
Weighted by skill, more accurate
48%
52%
46%
Sources: Estimize, IBES, S&P Capital IQ, FTSE Russell, Wolfe Research Luo’s QES
45% 54%
55%
10
d) Trading around Earnings Announcements •
•
•
•
Earnings announcement is among the most significant market moving corporate events As earnings release date approaches, the Estimate data becomes more and more accurate. Estimize provides timelier estimates, while sell-side analysts are better at predicting earnings over a longer horizon. If the consensus moves up right before earnings announcement, a company is more likely to beat the consensus.
Estimize EPS accuracy as a function of the # of days prior to earnings announcement Sell-side more accurate
50%
0% 0
1
IBES EPS
Estimize EPS
Actual EPS
2
3
4
5
6
7
8 9 10 11 12 13 # of days prior to earnings release
CIQ
Price
0.06
17.0
0.05
16.0
14
15
16
17
18
19
20
# of estimates for Pandora Media before earnings announcement
An example of Pandora Media before earnings announcement CIQ EPS
Estimize more accurate
100%
IBES
Estimize
60
0.03
14.0
0.02
13.0
0.01
12.0
dates
Sources: Estimize, IBES, S&P Capital IQ, FTSE Russell, Wolfe Research Luo’s QES
# of estimates
15.0
EPS
0.04
Stock price
50 40 30 20 10 0
dates
11
Earnings revisions prior to the announcement date leads announcement day return 𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠𝑅𝑒𝑣𝑖𝑠𝑖𝑜𝑛,,. =
𝐸𝑃𝑆,,. − 𝐸𝑃𝑆,,.34
𝐸𝑁𝑅𝑃,,. =
𝐸𝑃𝑆,,.34
𝐸𝑃𝑆,,. − 𝐸𝑃𝑆,,.34 𝑃𝑟𝑖𝑐𝑒,,.
Percentage of EPS changes in the week before earnings announcement
A) Most positive EPS revisions
B) Most negative EPS revisions
120%
0%
100%
-10% -20%
80%
-30%
60%
-40%
40%
-50% -60%
20%
-70%
0%
-80%
Sell-side top 1%
top 2%
Estimize FES top 5%
top 10%
Sell-side
bottom 1%
bottom 2%
Estimize FES
bottom 5%
bottom 10%
Average excess earning announcement day return
Sources: Estimize, IBES, S&P Capital IQ, FTSE Russell, Wolfe Research Luo’s QES
12
Post Earnings Announcement Drift (PEAD) •
PEAD is more significant, if a company beats (or misses) the Estimize estimates. PEAD decays away after two days.
Positive earnings surprise, first day PEAD
Negative earnings surprise, first day PEAD
0.6%
0.0%
0.5%
-0.1% -0.2%
0.4%
-0.3%
0.3%
-0.4%
0.2%
-0.5%
0.1%
-0.6% -0.7%
0.0% 10% surprise
20% surprise Estimize
30% surprise
40% surprise
10% surprise
20% surprise Estimize
Sell-side
30% surprise
40% surprise
Sell-side
Excess return when EPS misses over 40%
Excess return when EPS beats over 40%
0.2%
0.8%
0.0%
0.6%
-0.2%
0.4%
-0.4%
0.2%
-0.6%
0.0%
-0.8%
-0.2% day 1
day 2 Estimize
day 3
Sell-side
Sources: Estimize, IBES, S&P Capital IQ, FTSE Russell, Wolfe Research Luo’s QES
day 1
day 2 Estimize
day 3
Sell-side
13
e) Long-term Investment Strategies •
•
•
Benchmark earnings yield. For the benchmark factor, we use the consensus sell-side EPS, by taking a simple average of CIQ and IBES. Standard Estimize earnings yield. In this case, we replace the sell-side consensus with the Estimize estimates when we have at least three contributors in the Estimize database. Estimize FES earnings yield. Instead of replacing with the standard Estimize estimates, we use the FES model introduced in the previous section.
𝐹𝑄1 𝐸𝑃𝑆 𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠𝑌𝑖𝑒𝑙𝑑 = 𝑃𝑟𝑖𝑐𝑒
Cumulative performance, long/short quintile portfolio on S&P500 1.3
1.2
1.1
1.0
0.9
Benchmark earnings yield
Estimize Avg earnings yield
Estimize FES earnings yield
Monthly turnover
Sharpe ratio, different transaction cost assumptions
100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%
0.5 0.4 0.3 0.2 0.1
0.0 No cost Benchmark earnings yield
Estimize Avg earnings yield
Estimize FES earnings yield
Sources: Estimize, IBES, S&P Capital IQ, FTSE Russell, Wolfe Research Luo’s QES
2 bps
Benchmark earnings yield Estimize FES earnings yield
5 bps
Estimize Avg earnings yield
14
Enhanced Value Strategies • •
Enhanced earnings yield using Estimize data performs equally well in both large- and small-cap universes. Enhanced value strategies produce decent performance in a long-only context.
Sharpe ratio, different universe
Sharpe ratio, monthly versus daily rebalance 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0
1.4
1.2 1.0 0.8 0.6 0.4
0.2
Benchmark earnings yield
0.0
S&P 500
Russell 3000 Baseline
Russell 3000 sector neutral
3.0
Estimize FES earnings yield
Russell 3000 universe, monthly rebalance
Estimize
Long only portfolio performance, Russell 3000 universe
Estimize Avg earnings yield
Russell 3000 universe, daily rebalance
Sharpe Ratio, Long only portfolio Russell 3000 universe 1.4
2.5
1.2
2.0
1.0
1.5
0.8
1.0
0.6
0.5
0.4
0.0
0.2 Equally weighted Russell 3000
Top quintile Estimize earning yield
Sources: Estimize, IBES, S&P Capital IQ, FTSE Russell, Wolfe Research Luo’s QES
0.0 Equally weighted Russell 3000
Top quintile Estimize earning yield
15
4. Text Mining Unstructured Corporate Filing Data # of EDGAR Filings (Daily)
Average # of Words in the 10-K Filings
16 Sources: EDGAR, Wolfe Research Luo’s QES
Lazy Prizes
•
•
Firms in general use a well-defined format for their annual and quarterly filings. Almost identical language is simply repeated year after year, until someone actively intervenes and makes changes. When firms do break away from their tradition of textual descriptions in their filings, they typically foresee significant changes in their business, risk, or corporate strategy. Our NLP algorithms are able to identify potential issues from textual information among US transportation companies.
US Transportation Industry, Cumulative Performance
An Example: American Express 98 88
78
Price
•
Substantial and increasingly intense competition for partner relationships; risk surrounding Airline industry.
68 58 48
Ongoing legal proceedings can cause substantial monetary and reputational damages.
38
US Transportation Industry, Returns by Merciless
Sources: Bloomberg Finance LLP, EDGAR, FTSE Russell, S&P Capital IQ, Thomson Reuters, Wolfe Research Luo’s QES
17
Systematic Profiling EDGAR Composite (SPEC) Model SPEC model rank IC
Quintile portfolio returns
Long/short monthly turnover
SPEC model Long/short portfolio performance
Portfolio Sharpe ratio
Rank IC decay
Sources: Bloomberg Finance LLP, FTSE Russell, S&P Capital IQ, Thomson Reuters, Wolfe Research Luo’s QES
18
Interaction with traditional factors
•
•
•
•
•
Factor correlations with EDGAR composite models
Earnings Yield Earnings Revision Momentum 1M Reversal ROE EPS growth Market cap Volatility Composite 10K 11% 6% 4% 1% 10% -3% 11% -14% Composite 10Q 8% 3% 3% 1% 7% -2% 8% -10% Composite All 11% 5% 5% 2% 11% -2% 11% -13% Annualized returns of Style composites vs base factors 9 8 7
CAGR (%)
•
Lastly, it is interesting to note that our factor has almost no exposure to classic risk factors such as size, beta or volatility. One of the most important reasons of relying on alternative Big Data sources rests on the diversification benefit, in that they are expected to have minimal correlation with traditional factors. As expected, our signals based on EDGAR text mining are almost uncorrelated to any of our traditional factors. Finally, as a unique alpha source, the SPEC model should complement and add value to traditional factors. The performance of all traditional factors improves remarkably, when the SPEC is added. Growth factors witness the largest improvement. Improvement in risk-adjusted performance is even more significant. The Sharpe ratio improves by 50% for the majority of the conventional factors.
6 5
4 3 2 1 0 Earnings Revision
Earnings Revision composite
Earnings Yield
Earnings Yield composite
EPS growth EPS growth Momentum Momentum composite composite
ROE
ROE composite
ROE
ROE composite
Sharpe ratio of Style composites vs base factors 0.7 0.6
Sharpe ratio
•
0.5 0.4
0.3 0.2
0.1 0 Earnings Revision
Earnings Revision composite
Earnings Yield
Sources: Bloomberg Finance LLP, FTSE Russell, S&P Capital IQ, Thomson Reuters, Wolfe Research Luo’s QES
Earnings EPS growth EPS growth Momentum Momentum Yield composite composite composite
19
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