How important are the rules used to create smart beta portfolios

How Important Are The Rules Used To
Create Smart Beta Portfolios?
• How much did portfolio construction rules affect
performance?
• How should investors proceed?
Presented by:
Amitabh Dugar, Ph.D.
Ralph Goldsticker, CFA

Introduction
Most Smart Beta presentations are about:
“What and Why?”
Presentation addresses: “Do the rules used to
construct a Smart Beta portfolio matter?”
Approach:
Use alternative portfolio construction rules to
simulate multiple 25-year return histories for
Low Volatility, Fundamental Indexing and
Momentum strategies.
Compare average returns, risks, drawdowns
and factor exposures.
Source: 2

Proliferation of Smart Beta offerings
Number of US Large Cap Smart Beta ETFs
Smart Beta # of ETFs
Value 39
Dividends 29
Fundamental 6
Earnings 3
Revenues 1
Low Volatility, Low Risk 12
Equal Weight, Small Cap Tilt 7
Momentum 5
Quality 3
Total 66
Source: Dugar & Goldsticker classifications, ETF.com data, as of 5/1/2015 3
How do you choose?
How much does it matter?

Designing a smart beta strategy
Smart beta strategies are intended to provide
additional exposure to attractive characteristics.
But,
• True factors behind smart betas are not
observable.
• Investors also care about other portfolio
characteristics.
Managers design portfolio construction rules to:
• Maximize exposure to target characteristic
• Manage other characteristics and behavior
• Have attractive backtests
4

Large number of ways to construct any Smart
Beta strategy
5
Choices include:
• Metric and formula
• Universe: Geography and capitalization range
• Subset: All stocks, top 20%, top 50%, etc.
• Weighting: heuristic, optimized, cap, equal
• Tracking error aware: optimized, sector neutral
• Constraints, concentration, liquidity
• Reconstitution frequency
How do you decide?
How much does it matter?

Example: Decisions required to construct a low
volatility portfolio
6
Which measure of volatility?
• Standard deviation of total, excess, or residual returns; beta
• Estimation window: 1 year, 3 years, 5 years
How is the portfolio constructed?
• Heuristic rule-based
• All stocks weighted by inverse volatility
• Reweighted subset
• Which subset? All stocks; top 20%, 50%; sector neutral, …
• Which weights? Inverse vol, equal, cap, …
Optimized: Minimum volatility, equal risk weight, …
• Which covariance matrix?
• Sector neutral?
• Name constraints?
• Tracking error aware?

Similar issues with other Smart Betas
Fundamental indexing
• Which measure of value?
• Earnings, revenues, sales, cash flow, composite
• Which time period?
Momentum
• Which return window?
• Exclude most recent period?
• Scale returns by volatility?
• Which volatility?
• Which weighting scheme?
Quality
• Which measure of quality?
• Which weighting scheme
7

Evaluating the importance of Smart Beta
strategy design
Simulate multiple versions of: Low Volatility,
Momentum and Fundamental strategies
Evaluate how portfolio construction choices
affected simulated performance
• Returns
− Full period, sub periods
• Risk
− Volatility, beta, tracking error
− Factor exposures
− Drawdowns: absolute, relative
• Sharpe Ratio
8

1. Low Volatility case study
Metrics: standard deviation, residual risk,
beta
• Calculated using 1, 3 & 5 year windows
Universe: 500 largest US stocks
All 500 stocks, 250 lowest risk, 100 lowest risk
With and without sector neutrality constraint
25 years: 1989 – 2013, rebalanced quarterly
9

Low vol score depends on: Data window, total
vs residual risk, volatility vs beta
10
Source: Dugar & Goldsticker, CRSP, Compustat, 500 largest stocks, data ending 12/31/2013

Time window, partitioning and sector neutrality
had little impact on overall low vol performance
Source: Dugar & Goldsticker, CRSP, Compustat, 500 largest stocks, data ending 12/31/2013 11

Low vol strategies underperformed pre, and
outperfomed post bursting of Tech Bubble
12Source: Dugar & Goldsticker, CRSP, Compustat, 500 largest stocks, data ending 12/31/2013

Differences in portfolio characteristics of low vol
strategies were not large
Cap
Weight
Change Window used to Calculate Vol Partition Sector
Netural,
3 Yr Vol1/(1 Yr Vol) 1/(3 Yr Vol) 1/(5 Yr Vol)
250 Stocks
3 Yr Vol
100 Stocks
3 Yr Vol
Annual Return 11.34% 13.09% 12.92% 12.87% 11.97% 11.19% 12.96%
1989 - 1999 21.15% 17.27% 17.00% 16.85% 15.12% 14.18% 18.89%
2000 - 2013 4.73% 9.91% 9.82% 9.84% 9.55% 8.89% 8.51%
Volatility 16.1% 15.2% 15.4% 15.5% 13.3% 12.1% 16.0%
R-Square 94% 94% 94% 86% 76% 98%
Tracking Error 5.07% 5.31% 5.45% 6.84% 7.94% 3.45%
Sharpe Ratio 0.49 0.63 0.61 0.61 0.64 0.64 0.59
Beta 1.00 0.89 0.89 0.90 0.71 0.57 0.97
Up Beta 0.85 0.86 0.86 0.62 0.43 0.96
Down Beta 0.97 0.99 0.98 0.75 0.57 1.03
Max Drawdown -43% -42% -43% -43% -40% -35% -44%
Max Rel DD -38% -40% -41% -52% -56% -30%

Fama-French Factor Exposures
Market
Size
Small - Big
Value
High - Low Momentum
Cap Weighted 0.98 -0.16 0.01 -0.01
1 Year Volatility 0.89 0.00 0.27 -0.03
3 Year Volatility 0.90 -0.01 0.29 -0.05
5 Year Volatility 0.90 -0.01 0.30 -0.06
250 Stocks, 3 Year 0.76 -0.12 0.38 -0.02
100 Stocks, 3 Year 0.65 -0.21 0.37 0.01
Sector Neutral, 3 Yr 0.95 0.00 0.15 -0.02
Source: Dugar & Goldsticker, CRSP, Compustat, Ken French, 500 largest stocks, data ending 12/31/2013 14

Principle Component Exposures
PC 1:
Market
PC 2:
Momentum
PC 3:
???
PC 4:
Value
Cap Weighted 0.96 0.19 -0.04 0.09
1 Year Volatility 1.00 -0.03 0.03 0.01
3 Year Volatility 1.00 -0.04 0.03 0.03
5 Year Volatility 0.99 -0.05 0.03 0.04
250 Stocks, 3 Year 0.96 -0.28 0.01 -0.03
100 Stocks, 3 Year 0.87 -0.45 -0.03 -0.13
Sector Neutral, 3 Year 0.99 0.11 0.00 0.04

Low volatility case study findings:
Simulated returns:
All of the low volatility strategies we examined
exhibited similar patterns of returns.
Underperformed through 1999, outperformed since.
Investing in only the least volatile stocks produced less
volatile portfolios.
But, also lower returns, higher tracking error and
larger drawdowns.
Comparison of metrics:
Standard deviation, beta and tracking error produced
similar results.
1, 3 and 5 year statistics produced similar results.
Correlation between 1 and 5 year vol is only .5, but
dispersion is in higher vol stocks (low weights).
16

2. Fundamental Indexing Case Study
Metrics: Earnings, Dividends, Sales, Book Value;
combination of the four
Weighting scheme:
(Stock Fundamental) / (Aggregate Fundamental)
Look back: most recent 4, 8 & 12 quarters
Note: Partitioning the universe and sector
constraints were not investigated. They are
inconsistent with the philosophy behind
fundamental indexing.
17

Fundamental Indexing depends on metric and look-back
But shows less variability than other strategies

Similar performance of fundamental strategies
Book value diverged post Financial Crisis

Fundamental strategies underperformed pre,
and outperfomed post bursting of Tech Bubble

Characteristics of fundamentally weighted
strategies were similar
Cap Wt Book Earnings Revenues Dividends Comb 8Q
Annual Return 11.21% 10.70% 11.54% 11.71% 11.36% 11.44%
1989 - 1999 20.36% 17.77% 17.73% 17.40% 16.21% 17.67%
2000 - 2013 4.25% 5.23% 6.71% 7.26% 7.53% 6.59%
Volatility 16.2% 17.4% 15.5% 16.4% 15.0% 16.0%
R-Square 91% 92% 90% 81% 92%
Tracking Error 5.2% 4.3% 5.3% 6.6% 4.6%
Sharpe Ratio 0.48 0.42 0.52 0.51 0.53 0.50
Beta 1.00 1.02 0.91 0.95 0.83 0.94
Up Beta 1.02 0.92 0.97 0.82 0.95
Down Beta 1.04 0.89 1.01 0.76 0.93
Max Draw Down -44.3% -53.6% -47.9% -49.0% -50.1% -49.9%
Max Rel DD -21.7% -22.0% -24.5% -32.6% -22.4%

Market
Size
Small - Big
Value
High - Low Momentum
Cap Weighted 0.98 -0.16 0.01 -0.01
Book 1.01 -0.12 0.30 -0.16
Earnings 0.91 -0.18 0.23 -0.12
Revenue 0.94 -0.04 0.30 -0.11
Dividend 0.87 -0.25 0.37 -0.12
Comb 8Q 0.94 -0.15 0.28 -0.13

PC 1:
Market
PC 2:
Momentum
PC 3:
???
PC 4:
Value
Cap Weighted 0.96 0.19 -0.04 0.09
Book 0.97 0.04 0.02 0.22
Earnings 0.98 -0.03 0.00 0.19
Revenue 0.97 0.01 0.03 0.17
Dividend 0.95 -0.20 -0.01 0.17
Comb 8Q 0.98 -0.02 0.01 0.19

Fundamental Indexing case study findings:
24
Simulated returns:
All simulations exhibited similar return patterns
• Underperformed through 1999
• Sharp recovery
• Generally, performed in line since
Book Value had more severe drawdown in
Financial Crisis, and less recovery since.
Generally independent of data window.
While there are some differences, all metrics
tend to produce similar stock weights

3. Momentum case study
Metrics: 5 month, 11 month & 23 month returns
• Periods end 1 month before portfolio construction
• Convert raw momentum values to Z-Score
Weighting scheme:
• Z > 0: 1 + Z; Z < 0: 1/(1-Z); Z=0: 1
All 500, 250 highest Z-Score, 100 highest Z-Score
With and without sector neutrality constraint
25

Momentum weights depend on look-back
window and return vs Z-Score

More concentrated portfolios had superior long-
term performance, but also larger drawdowns

Outperformance of momentum primarily came
post bursting of Tech Bubble

Differences in portfolio characteristics of
momentum strategies were not large.
Cap
Weight
Window used to Calculate Momentum Partition 11 Month Sector
Netural,
11 Mth5 Month 11 Month 23 Month 250 Stocks 100 Stocks
Annual Return 11.34% 12.98% 14.17% 13.52% 12.83% 12.48% 13.8%
1989 - 1999 21.15% 17.72% 21.69% 20.97% 17.35% 17.01% 21.12%
2000 - 2013 4.73% 9.38% 8.59% 8.00% 9.39% 9.04% 8.30%
Volatility 16.1% 17.2% 17.5% 17.7% 17.4% 18.4% 17.2%
R-Square 95% 96% 97% 93% 88% 98%
Tracking Error 5.26% 4.62% 4.05% 6.40% 8.73% 3.69%
Sharpe Ratio 0.49 0.55 0.61 0.57 0.54 0.49 0.60
Beta 1.00 1.02 1.04 1.07 1.00 1.00 1.06
Up Beta 0.97 1.03 1.05 0.93 0.92 1.09
Down Beta 1.14 1.15 1.16 1.13 1.13 1.08
Max Drawdown -43% -44% -44% -45% -43% -46% -44%
Max Rel DD -37% -31% -30% -41% -48% -32%

Market
Size
Small - Big
Value
High - Low Momentum
Cap Weighted 0.98 -0.16 0.01 -0.01
5 Mth Momentum 1.00 0.09 0.21 0.02
11 Mth Momentum 1.04 0.13 0.09 0.13
23 Mth Momentum 1.05 0.06 0.06 0.09
250 Stocks, 11 Mth 1.04 0.18 0.04 0.28
100 Stocks, 11 Mth 1.08 0.30 -0.02 0.43
Sect Neutral, 11 Mth 1.02 0.07 0.06 0.09

PC 1:
Market
PC 2:
Momentum
PC 3:
???
PC 4:
Value
Cap Weighted 0.96 0.19 -0.04 0.09
5 Mth Momentum 0.98 0.15 0.01 -0.03
11 Mth Momentum 0.96 0.27 -0.02 -0.08
23 Mth Momentum 0.96 0.24 -0.01 -0.05
250 Stocks, 11 Mth 0.91 0.35 -0.07 -0.18
100 Stocks, 11 Mth 0.83 0.45 -0.12 -0.27
Sect Neutral, 11 Mth 0.96 0.26 -0.02 -0.04

Momentum case study findings
32
Simulated returns:
All of the momentum strategies we examined
exhibited similar patterns of returns
• Most of outperformance came post Tech Bubble
• Underperformance during Financial Crisis
• Outperformance since
Full simulation: 11 month outperformed 5
5 month momentum worst performer before and
during Tech Bubble.
More concentrated portfolios had larger factor
exposures and performance cycles.

Smart Beta factor exposures across sleeves
Fama-French
Principle Components
Source: 33

Market
Size
Small - Big
Value
High - Low Momentum
Capitalization
Weighted
0.98 -0.16 0.01 -0.01
3 Year Volatility 0.90 -0.01 0.29 -0.05
100 Lowest 3 Year
Volatility
0.65 -0.15 0.37 0.04
Fundamental
Indexing
0.94 -0.15 0.28 -0.13
11 Month
Momentum
1.04 0.13 0.09 0.13
100 Highest 11 Mth
Momentum
1.08 0.30 -0.02 0.43

Principle Components Analysis
PC 1:
Market
PC 2:
Momentum
PC 3:
???
PC 4:
Value
Capitalization
Weighted
0.96 0.19 -0.04 0.09
3 Year Volatility 1.00 -0.04 0.03 0.03
100 Lowest 3 Year
Volatility
0.87 -0.45 -0.03 -0.13
Fundamental
Indexing
0.98 -0.02 0.01 0.19
11 Month
Momentum
0.96 0.27 -0.02 -0.08
100 Highest 11 Mth
Momentum
0.83 0.45 -0.12 -0.27

Summary of simulated returns
For each Smart Beta, results were largely
insensitive to the portfolio construction rules.
• Average returns
• Drawdowns
• Behavior during Tech Bubble and Financial Crisis
100 and 250 stock portfolios
• Larger drawdowns and cycles of relative
performance
• More extreme factor exposures
Sector neutral portfolios
• Less tracking error
Source: 36

Conclusions:
Pick the approach for economic reasons.
Don’t rely on backtests.
Why do you want the exposure?
• What risk or behavior is it intended to capture?
• Pick approach most closely aligned with objective.
Consider exposures to unintended risks.
How does the strategy fit into your aggregate portfolio?
Factor exposures
Is its behavior complimentary?
Pay attention to other portfolio characteristics.
Capacity, liquidity and concentration
Turnover and transactions costs
Management fees
37

How important are the rules used to create smart beta portfolios

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