Weekly Portfolio Review - Arizona State University...Neil Jha. John Remy. Team Introduction....
Transcript of Weekly Portfolio Review - Arizona State University...Neil Jha. John Remy. Team Introduction....
Semi-Annual PresentationFriday, April 26th, 2019
Team A - Undergraduate Student Investment Management Fund
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FundManager
Drew Kudlinski
FundAnalysts
Jenna Snittjer Vishnu Patel John MichaelHayes
Andrew Fox Neil Jha John Remy
Team Introduction
Investment Thesis
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Investment Thesis
Event-based portfolio based upon “Predictable Corporate Distributions and Stock Returns” by Hendrik Bessembinder and Feng Zhang The Review of Financial Studies, 2014
Dividend increase at 𝒕𝒕 − 𝟏𝟏𝟏𝟏 → Dividend increase likely in 𝒕𝒕Special dividends at 𝒕𝒕 − 𝟏𝟏𝟏𝟏 → Special dividends likely in 𝒕𝒕
Stock dividend at 𝒕𝒕 − 𝟏𝟏𝟏𝟏 → Stock dividend likely in 𝒕𝒕
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Frequency of Follow-On Events
0
2000
4000
6000
8000
10000
12000
14000
0 3 6 9 12 15 18 21 24 27 30 33 36
Num
ber o
f Occ
urre
nces
Months Since Event
Dividend Increases
0
500
1000
1500
2000
2500
3000
3500
4000
0 3 6 9 12 15 18 21 24 27 30 33 36
Num
ber o
f Occ
urre
nces
Months Since Event
Stock Dividends
0
500
1000
1500
2000
2500
3000
0 3 6 9 12 15 18 21 24 27 30 33 36
Num
ber o
f Occ
urre
nces
Months Since Event
Special Dividends
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Conditional Probabilities
P(t) P(t)
P(t|t-12) P(t|t-12)
P(t)P(t|t−12)
P(t)P(t|t−12)
Dividend Increases Special DividendsStock Dividends
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Daily Stock Returns
0.00%
0.20%
0.40%
0.60%
0.80%
-21 -18 -15 -12 -9 -6 -3 0 3 6 9 12 15 18 21
Retu
rns
Day
Daily Stock Returns Around Event AnnouncementsDividend Increases
Strategy Implementation
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Conditional Probabilities
Conditional Probabilities
Bessembinder / Zhang
Event Types P(t) P(t|t-12) P(t|t-12, t-24) P(t|t-12, t-24, t-36) P(t|t-12, t-24, t-36, t-48)
Dividend Increase 1.2% 32.9% 55.3% 62.7% 66.8%
Special Dividend 0.2% 32.6% 59.9% 65.0% 68.1%
Stock Dividend 0.4% 29.2% 57.6% 64.6% 68.8%
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Certainty Buckets
Certainty Bucket Minimum Occurrences Holding Period
Monthly -- 1 Month
Weekly 4 9 Weekdays
Daily 4 5 Weekdays
SUNDAY MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY SATURDAY29 30 31 01 02 03 04
05 06 07 08 09 10 11
Daily Buy Daily Certainty Daily Sell
12 17 18
Weekly Buy19 20 21 22 23 24 25
Weekly Certainty26 27 28 29 30 31 01
Weekly Sell
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Holding Period Visualization
Realization Rates
December
Confidence Predicted Realized Hit Rate Confidence
Daily 10 10 100% 70%
Weekly 14 13 93% 77%
Monthly 28 26 93% 100%
Month of November – 81.5% overall hit rate Month of December – 94.2% overall hit rate
Month of January – 66.0% overall hit rate
January
Confidence Predicted Realized Hit Rate Confidence
Daily 8 7 88% 57%
Weekly 6 4 67% 100%
Monthly 39 24 62% 100%
February
Confidence Predicted Realized Hit Rate Confidence
Daily 15 13 87% 62%
Weekly 27 26 96% 85%
Monthly 61 53 87% 100%
Month of March – 80.0% overall hit rate
12
Month of February – 89.3% overall hit rate
March
Confidence Predicted Realized Hit Rate Confidence
Daily 7 6 86% 50%
Weekly 1 1 100% 100%
Monthly 17 13 76% 100%
April
Confidence Predicted Realized Hit Rate Confidence
Daily 13 6 46% 50%
Weekly 7 3 43% 67%
Monthly 27 9 33% 100%
Month of April – 38.3% overall hit rate *
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January
Confidence Predicted Realized Hit Rate Confidence
Daily 2 2 100% 50%
Weekly 2 2 100% 100%
Monthly 23 18 78% 100%
*as of April 24th 2019
Overview of Results
Total Portfolio Returns: Gross vs. Russell 3000
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-15.0%
-10.0%
-5.0%
0.0%
5.0%
10.0%
15.0%
11/19/2018 12/19/2018 1/19/2019 2/19/2019 3/19/2019 4/19/2019
November 19th, 2018 - April 23rd, 2019
Portfolio Russell 3000
∆ =+1.88%
Turnover By Month
15
Month Quantity of Trades TurnoverContribution to Turnover
by Russell
November $971,479.58 129.53% 34.68%
December $1,603,733.05 213.83% 29.57%
January $484,258.23 64.57% 32.31%
February $742,745.71 99.03% 22.57%
March $1,301,997.02 173.60% 21.34%
April $1,309,662.99 174.62% 17.99%
Total $6,413,876.58 855.18% 219.81%
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-15.0%
-10.0%
-5.0%
0.0%
5.0%
10.0%
15.0%
11/19/18 12/19/18 01/19/19 02/19/19 03/19/19 04/19/19
November 19th, 2018 - April 23rd, 2019
Portfolio Russell 3000
Total Portfolio Returns: Net of Trading Costs
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∆ =+1.20%
Active Portfolio Returns: Gross Active Returns vs. Russell 3000
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-15.0%
-10.0%
-5.0%
0.0%
5.0%
10.0%
15.0%
11/19/2018 12/19/2018 1/19/2019 2/19/2019 3/19/2019 4/19/2019
November 19th, 2018 - April 23rd, 2019
Active Portfolio Russell 3000
∆ =+3.19%
-15.0%
-10.0%
-5.0%
0.0%
5.0%
10.0%
15.0%
11/19/2018 12/19/2018 1/19/2019 2/19/2019 3/19/2019 4/19/2019
November 19th, 2018 - April 23rd, 2019
Portfolio Russell 3000
Active Portfolio Returns: Net Active Returns vs. Russell 3000
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∆ =+2.61%
Shadow Portfolio Benchmarking
Shadow Portfolios: Monthly Rebalance (EW)
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Certainty Bucket Minimum Occurrences Holding Period
Monthly -- 1 Month
Weekly 4 9 Weekdays
Daily 4 5 Weekdays
Bessembinder and Zhang Portfolio Construction
SIM Fund Strategy Implementation Using “Certainty Buckets”
Prior Corporate Event Announcements
Portfolios of All Securities in a Given
MonthMonthly Holding
Periods
Shadow Portfolios: Gross Active Only vs. Monthly Rebalance (EW)
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-15.0%
-10.0%
-5.0%
0.0%
5.0%
10.0%
15.0%
11/19/2018 12/19/2018 1/19/2019 2/19/2019 3/19/2019 4/19/2019
November 19th, 2018 - April 23rd, 2019
Active Portfolio Monthly Rebalance (EW)
∆ =+1.42%
Shadow Portfolios: Perfect Foresight (EW)
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Conditional Probabilities
Bessembinder / Zhang
Event Types P(t) P(t|t-12) P(t|t-12, t-24) P(t|t-12, t-24, t-36) P(t|t-12, t-24, t-36, t-48)
Dividend Increase 1.2% 32.9% 55.3% 62.7% 66.8%
Special Dividend 0.2% 32.6% 59.9% 65.0% 68.1%
Stock Dividend 0.4% 29.2% 57.6% 64.6% 68.8%
But what if prediction accuracy was 100%?
Shadow Portfolios: Perfect Foresight (EW) vs. Russell 3000
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-20.0%
-15.0%
-10.0%
-5.0%
0.0%
5.0%
10.0%
15.0%
20.0%
11/12/2018 12/12/2018 1/12/2019 2/12/2019 3/12/2019 4/12/2019
November 19th, 2018 - April 23rd, 2019
Perfect Foresight (EW) Russell 3000
∆ =+5.35%
Event Time Returns – Equal Weighted (bps)
24
Excess Returns (bps)
0
50
100
150
200
-80.0
-60.0
-40.0
-20.0
0.0
20.0
40.0
60.0
-1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Equal-Weighted Excess Returns with Security Count
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-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
-1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Equal Weighted T-Scores by Day
T-Scores
192 192 192 189 173 157 153 141 116 83 82 71 62 44 32 32 24 15Security Count
Event Time Returns – Day 4
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Event Time Returns – Value Weighted (bps)
26
Excess Returns (bps)
192 192 192 189 173 157 153 141 116 83 82 71 62 44 32 32 24 150
50
100
150
200
-80.0
-60.0
-40.0
-20.0
0.0
20.0
40.0
60.0
-1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Value-Weighted Excess Returns with Security Count
April 26th, 2019 Student Investment Management Fund - Team A
-4.0-3.0-2.0-1.00.01.02.03.04.05.0
-1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Value Weighted T-Score By Day
T-Scores
Security Count
Appendix
Average Sector Exposures
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20.98%
16.99% 16.58%
11.58%
9.49%
6.15%5.50% 5.26%
4.49%
1.94%1.03%
13.85%
3.91%
10.01% 10.38%
3.16%
20.23%
2.95%
14.59%
6.38%
9.25%
4.98%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
Financials Industrials Real Estate Utilities ConsumerDiscretionary
InformationTechnology
Health Care Materials ConsumerStaples
CommunicationServices
Energy
Portfolio Russell 3000
Sector Return Attribution
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1.7%
7.3%
(0.3)%
1.9%
4.6%
49.9%
11.6%
9.5%
9.1%
6.7%
6.5%
4.6%
8.1%
11.8%
15.2%
6.8%
22.8%
5.3%
(1.4)%
2.8%
19.0%
3.0%
(10.0)% 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0%
Financials
Industrials
Real Estate
Utilities
Consumer Discretionary
Information Technology
Health Care
Materials
Consumer Staples
Communication Services
Energy
Russell 3000 Portfolio
Sector Return Contribution
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1.4%
0.2%
1.0%
0.8%
0.7%
1.5%
0.6%
(0.2)%
0.3%
0.2%
0.2%
0.9%
0.3%
1.1%
1.6%
0.2%
4.4%
0.1%
(0.1)%
0.1%
1.8%
0.0%
(1.0)% 0.0% 1.0% 2.0% 3.0% 4.0% 5.0%
Financials
Industrials
Real Estate
Utilities
Consumer Discretionary
Information Technology
Health Care
Materials
Consumer Staples
Communication Services
Energy
Russell 3000 Portfolio
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Undergraduate Student Investment Management FundSpring 2019 PresentationFriday, April 26
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Team Overview
Fund Manager
Fund Analysts
Jerry Zhang
Yanni DongNicholas DeSantangiloMichael Calabro Abbas Haider
Lukas Stogsdill Li Yang Isaac Zolot
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Investment Thesis & Implementation
Thesis Rebalance & ReturnsConstruction Shadow
Portfolios
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Momentum Returns
Momentum theory posits that past winners will continue to win while past losers will continue to lose.
Source: Kenneth R. French Data Library
Thesis Rebalance & ReturnsConstruction Shadow
Portfolios
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Continuous vs. Discrete Returns
Notice reversals and
shocks
Notice difference in magnitude of daily
changes
Notice same
beginning and end
price
Source: Da, Gurun, & Warachka (2014)
Thesis Rebalance & ReturnsConstruction Shadow
Portfolios
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Calculating Information Discreteness
Our fund’s investment thesis is derived from Da, Gurun, & Warachka’s (2014) analysis on momentum investing conditioned on information discreteness.
Information Discreteness
Sign of the cumulative return
over t-12 to t-2
Percentage of days during
formation period with negative returns minus
positive returns
Source: Da, Gurun, & Warachka (2014)
ID = sign(Annual Return) × [%neg-%pos]
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Discussion on Investment Quintiles
“Losers”Min %
Quintile 4 Quintile 3 Quintile 2 “Winners”Max %
Sort by Annual Return
Sort by ID
(570 stocks)
Winners Losers
Filter by:● Market Cap Limits● Charter Constraints● News Checks
Quintile 2Quintile 3Quintile 4“Losers” “Winners”
Investable Universe
(114 stocks)
Russell 3000
Thesis Rebalance & ReturnsConstruction Shadow
PortfoliosConstructionThesis
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Portfolio Construction Process
Cumulative market cap with respect to target portfolio: Consumer Discretionary
Target Portfolio Characteristics Example: Consumer Discretionary
Security contribution (%) to portfolio sector weighting
Single Security Actual Market Cap
Target Aggregate Market Cap:$350B
=
Target # of Stocks In Our Portfolio:70
Target Aggregate Market Cap:$350B
Rebalance & Returns
Shadow PortfoliosConstructionThesis
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Portfolio Construction Process
Security Market Cap ($B) Market Cap Weighting ID Rank
A 6.84 2.00% 1
B 5.59 1.60% 2
C 9.64 2.75% 3
D 13.26 3.79% 4
Cumulative market cap with respect to target portfolio: Consumer Discretionary
0.0 2.0 4.0 6.0 8.0 10.0 12.0
Target Sector Weighting
Rebalance & Returns
Shadow PortfoliosConstructionThesis
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Portfolio Construction Process
Cumulative market cap with respect to target portfolio: Consumer Discretionary
0.0 2.0 4.0 6.0 8.0 10.0 12.0
Security Market Cap ($B) Market Cap Weighting ID Rank
A 6.84 2.00% 1
B 5.59 1.60% 2
C 9.64 2.75% 3
D 13.26 3.79% 4
Target Sector Weighting
Rebalance & Returns
Shadow PortfoliosConstructionThesis
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Portfolio Construction Process
Cumulative market cap with respect to target portfolio: Consumer Discretionary
0.0 2.0 4.0 6.0 8.0 10.0 12.0
Security Market Cap ($B) Market Cap Weighting ID Rank
A 6.84 2.00% 1
B 5.59 1.60% 2
C 9.64 2.75% 3
D 13.26 3.79% 4
Target Sector Weighting
Rebalance & Returns
Shadow PortfoliosConstructionThesis
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Portfolio Construction Process
Cumulative market cap with respect to target portfolio: Consumer Discretionary
0.0 2.0 4.0 6.0 8.0 10.0 12.0
Security Market Cap ($B) Market Cap Weighting ID Rank
A 6.84 2.00% 1
B 5.59 1.60% 2
C 9.64 2.75% 3
D 13.26 3.79% 4
Target Sector Weighting
Rebalance & Returns
Shadow PortfoliosConstructionThesis
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Portfolio Construction Process
Cumulative market cap with respect to target portfolio: Consumer Discretionary
0.0 2.0 4.0 6.0 8.0 10.0 12.0
Security Market Cap ($B) Market Cap Weighting ID Rank
A 6.84 2.00% 1
B 5.59 1.60% 2
C 9.64 2.75% 3
D 13.26 3.79% 4
Target Sector Weighting
Rebalance & Returns
Shadow PortfoliosConstructionThesis
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Rebalancing
Q5 Q4 Q3 Q2 Q1
Q1
Q2
Q3
Q4
Q5
ID
Annual Return
Hold
Buy
Sell
Hold Range
Buy Range
Shadow PortfoliosThesis Rebalance
& ReturnsConstruction
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Returns and Attribution
Shadow PortfoliosThesis Rebalance
& ReturnsConstruction
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Market Cap Tilt
Russell 3000159.2 M 1.012 T
5.0 B
Our Portfolio500.0 M
159.2 M
24.4 B
Russell 2000
Shadow PortfoliosThesis Rebalance
& ReturnsConstruction
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Portfolio Returns
Our Portfolio 10.87%
Russell 3000 10.36%
Russell 2000 6.53%
Shadow PortfoliosThesis Rebalance
& ReturnsConstruction
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Sector Contribution
Shadow PortfoliosThesis Rebalance
& ReturnsConstruction
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Selection
Shadow PortfoliosThesis Rebalance
& ReturnsConstruction
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Information Technology Sector Attribution
Shadow PortfoliosThesis Rebalance
& ReturnsConstruction
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Shadow Portfolios
Thesis Construction Shadow Portfolios
Rebalance & Returns
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Shadow Portfolios
News Count Momentum03 ● Sort by Annual Return
● Sort by Count of Press Mentions
Sector Agnostic02● Sort by Annual Return● Sort by ID● Sector Agnostic
Pure Momentum01 ● Sort by Annual Return● Purchase Top 60 Securities
Our Portfolio00● Sort by Annual Return● Sort by ID● Sector Neutral
Thesis Construction Shadow Portfolios
Rebalance & Returns
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Shadow Portfolio Returns
Sector Agnostic 14.38% 6.62 σ2
News Count Momentum 12.65% 6.29 σ2
Pure Momentum 13.00% 7.53 σ2
Our Portfolio 10.87% 5.64 σ2
Thesis Construction Shadow Portfolios
Rebalance & Returns
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Thank you. Any questions?
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Appendix
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Turnover
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Outperformance
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Information Technology Portfolio Weights
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Sector Returns
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Sector Weights
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Allocation
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Discussion on Investment Quintiles
“Losers”Min: -95%
Quintile 4-18% to -3%
Quintile 3-3% to 11%
Quintile 211% to 33%
“Winners”Max: 1648%
Sort by Annual Return
Sort by ID
(570 stocks)
Winners Losers
Filter by:● Market Cap Limits● Charter Constraints● News Checks
Quintile 2Quintile 3Quintile 4“Losers” “Winners”
Investable Universe
(114 stocks)
Russell 3000
Thesis Rebalance & ReturnsConstruction Shadow
PortfoliosConstructionThesis