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7/29/2019 Asset Allocation Overview- Callan Research.pdf
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0Independent Adviser Group
Mark AndersenVice President415-274-3023 [email protected]
Callan Associates
101 California StreetSuite 3500San Francisco, CA 94111
The Chemistry of Asset Allocation
Are Traditional Asset Classes the Right Building
Blocks for Portfolio Diversification?
2011 Callan IAG National Conference
September 2011
7/29/2019 Asset Allocation Overview- Callan Research.pdf
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Agenda
Overview of traditional approaches
Benefits and shortfalls
Implementation
Introduction to factor-based allocation
Classifying factors
Combining factors
Implementation
A hybrid approach to asset allocation
Economic Scenario Buckets
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Why theInterest in Factors?
Traditionally portfolios are built with asset classes such as equity, fixed income,
real estate, etc.
Ideally portfolios would be made up of many components, each independently
risky, and each compensated with return for taking risk.
It is possible to break down asset classes into building blocks, or factors, which
explain the majority of their return and risk characteristics.
Asset classes are an indirect way to invest in factors, but it is possible to invest
directly in certain factors.
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Asset classes are bundles of exposures divided into categories such as
equities, bonds, real assets, etc.
Asset classes should be as independent as possible (minimal overlap) and in
aggregate should cover the investment universe (minimal gaps).
Correlations among asset classes are driven by many common factor
exposures.
The Basics Why the Interest in Factors?
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The finer the distinctions between various asset classes, the higher the resultingcorrelations.
Typical asset allocation relies on sub-asset classes (such as large cap or small capU.S. equity or non-U.S. developed equity).
Equity
U.S.
Large
Small
Non-
U.S.Developed
Emerging
Debt
U.S.
Investment
Grade
HighYield
Non-
U.S.Developed
Emerging
Asset Class
Sub-Asset Class
The BasicsDistinction Between Asset Classes
and Sub-Asset Classes
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The output of a mean-variance optimizer is a frontier composed of efficientportfolios.
Portfolios are classified as efficient if they provide the greatest expectedreturn for a given level of expected risk.
Optimization, and efficient portfolios, rely heavily on the quality of the inputs
Capital market forecasts are the basis of this type of model.
The Basics Mean Variance Optimization
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The frontier is composed of efficient portfolios across the risk spectrum.
Less than 100% correlation among asset classes lead to diversification benefits whichefficient portfolios maximize.
0% 5% 10% 15% 20% 25% 30% 35%
0%
2%
4%
6%
8%
10%
U.S.
Equity
Non-U.S. Equity
U.S. Fixed Income
Real Estate
PrivateEquity
Cash
Emerging Markets
Equity
Expected Risk (Standard Deviation)
Expec
tedReturn
24% U.S. Equity
13% Non-U.S. Equity5% Emerging Markets
48% Fixed Income
7% Real Estate
3% Private Equity
E(r) = 6.6%, E() = 10%
Traditional Approach Classic Efficient Frontier
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7/29/2019 Asset Allocation Overview- Callan Research.pdf
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Correlations across asset classes can be high because many asset classes are
exposed to similar or common risk factors.
For example, U.S. equity and U.S. corporate bonds share some common exposures, as doprivate equity and public equity.
U.S. Equity U.S. Corporate Bonds
GDPGrowth
Value Liquidity
Size Inflation
Volatility
Currency
Momentum
RealRates
Liquidity
DurationDefault
Risk
Inflation Volatility
Currency
Capital
Structure
Traditional Approach Some Limitations
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If asset classes are molecules, then factors are atoms.
Those factors aggregate into the risk return characteristics of asset classes.
We are interested in identifying and classifying various factors and exploring
how they can be used to allocate assets.
What Are Factors?
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Factors are the basic exposures which make up investments.
They can be thought of as building blocks or risk premiums.
Periodic Table of Factors
Macroeconomic Regional Equity Fixed Income Other
GDP GrowthSovereign
ExposureSize Duration Liquidity
Product ivit y Currency Value Convexity Leverage
Real Interest
Rates
Emerging
Markets
(Institutions +Transparency)
Momentum Credit Spread Real Estate
Inflation Default Risk Commodities
VolatilityCapital
StructurePrivate Markets
Classifying Factors A Sampling
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Ironically, even though risk factors are the basic building blocks of investments,
there is no natural way of investing in many of them directly.
Most risk factors can be accessed through derivatives or long/shortpositions.
Simple examples:
Inflation: Nominal Treasuries less TIPS
Real Interest Rates: TIPS
Volatility: VIX futures
More complex examples:
Value: Long Developed Equity Value, Short Developed Equity Growth
Size: Long Developed Equity Small Cap, Short Developed Equity LargeCap
Credit Spread: Long U.S. High Quality Credit, Short U.S.Treasury/Government
Duration: Long U.S. Treasury 10+ Year, Short US Treasury 1-3 Year
Emerging Markets: Long Emerging Equity, Short Developed Equity
Not exactly possible:
GDP Growth, Productivity, Momentum, Leverage
Risk Factors How can one get exposure?
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Factor Risk and Return
Factor CorrelationsEquity Value Size EM HY Spread Default Duration Real Rates Inflation Volatility
Equity 1.00
Value 0.07 1.00
Size 0.39 0.15 1.00
EM 0.43 -0.14 0.42 1.00HY Spread 0.69 0.06 0.40 0.41 1.00
Default 0.54 0.03 0.38 0.34 0.74 1.00
Duration -0.18 0.12 0.07 -0.04 -0.38 -0.28 1.00
Real Rates 0.09 -0.04 0.32 0.19 -0.02 0.21 0.57 1.00
Inflation -0.43 0.12 -0.41 -0.35 -0.49 -0.60 0.12 -0.67 1.00
Volatility -0.69 0.04 -0.22 -0.32 -0.49 -0.40 0.15 -0.03 0.32 1.00
Correlation < -0.3 Correlation between 0.4 and 0.6 Correlation > 0.6
Factor Exposure Long Position Short Position Return Risk
Equity MSCI World 3.20% 17.58%
Value MSCI World Value MSCI World Growth 1.07% 6.31%
Size MSCI World Small Cap MSCI World Large Cap 8.80% 8.22%
EM MSCI Emerging Markets MSCI World 13.69% 11.47%
HY Spread BC High Yield BC Intermediate Credit (IG) 2.57% 9.97%
Default BC Aaa BC BBB 1.22% 5.08%
Duration BC 20+ Year Treasuries BC 1-3 Year Treasuries 2.51% 11.95%Real Rates BC TIPS 7.03% 6.86%
Inflation BC Treasuries BC TIPS -1.74% 5.05%
Volatility CBOE VIX -4.05% 66.54%
Historical
Based on 10 Years of Monthly Data ending 12/31/2010
Risk FactorsReturn, Volatility, and
Correlation Historical Experience
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Factor returns (or premiums) are fairly low, most have returned less than 5% overthe past decade.
Factor characteristics are extremely time sensitive, so varying the data horizoncan materially impact relationships.
Factor standard deviations range widely between 5% to 66% (for the VIX).
Correlations among factors are low, typically ranging from -0.5 to +0.6.
Relatively highly correlated factors include Equity vs. High Yield and HighYield vs. Default.
The average correlation for the 10 factors on the previous page is 0.03.
This is significantly less than many asset class correlations which range from -
0.15 to more than +0.90.
Sub-asset classes like U.S. Small Cap vs. U.S. Large Cap are the mostcorrelated while relatively unrelated pairings such as U.S. 1-3 YearTreasuries vs. Private Equity are also the least correlated.
Factor Assumptions Notes on the Historical Experience
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Equal weighting 10 sample factors into a portfolio with monthly rebalancing results in historicalequity-like returns with considerably less risk.
Even with this simple weighting scheme, the factor portfolio exhibits relatively low volatility vs. atypical 60/40 portfolio.
Both portfolios have a very low correlation with each other (-0.18).
40%Russell 3000
40%BC Aggregate
20%MSCI ACWI ex-US
vs.
Historical Return: 5.16% 5.66%
Historical Risk: 11.16% 5.75%
Traditional 60/40 Portfolio
Value
Size
EM
HY
SpreadDefault
Duration
Real Rates
Inflation
Volatility
Factor Portfolio
Based on 10 Years of Monthly Data ending 12/31/2010
Equity
Traditional vs. Factor Portfolio comparison
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Optimizing a factor portfolio using historical risk, return, and correlations results in a best-fit portfolio tuned for the 10 year data sample.
This portfolio was selected from the efficient frontier based on its 5.75% expected risk.
This example is meant to illustrate that optimization with factors is possible, but high qualityforward-looking inputs are necessary.
Historical Return: 5.66% 10.25%
Historical Risk: 5.75% 5.75%
Equity
Value
Size
EM
HY
SpreadDefault
Duration
RealRates
Inflation
Volatility
Simple Factor Portfolio
Real Rates
VolatilityValue
Size
EM
OptimizedFactorPortfolio
vs.
Based on 10 Years of Monthly Data ending 12/31/2010
Simple vs. Optimized Portfolio Comparison
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Which factors to include?
How to weight factors?
Factor portfolios must be implemented using long and short exposures, oftenvia derivatives allocations.
Active high frequency rebalancing among a large number of risk factors isnecessary to maintain the desired exposure.
Factor portfolios resemble certain styles of hedge funds and risk parityapproaches (sans leverage).
Portfol io Construction Factor Portfolios Present Notable Challenges
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Examine asset classes through a factor lens and group like asset classes
together under macroeconomic scenarios.
Inflation
Eco
nomicGrowth
Low or Falling Growth
High or Rising Inflation
Inflation Linked Bonds (TIPS)
Commodities
Infrastructure
HighGrowth
HighInflation
Real Assets: Real Estate,Timberland, Farmland,Energy, MLPs
LowGrowthLowInflationorDeflation
Cash
Government Bonds
HighGrowthLowInflation
Equity
Corporate Debt
A Hybrid Approach Can We Still Apply Factors to Traditional Methods?
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Bucketing asset classes based on their response to macroeconomic scenarioscombines the transparency of investing with asset classes with the granularity offactor-based approaches.
Sample Groupings:
Capital Growth: Grow assets through relatively high long-term returns
Global equity
Private equity
Income / Flight to Quality: Provide current income and protect capital in times
of market uncertainty
Global fixed income
Cash equivalents
Volatility Hedge: Earn returns between stocks and bond while attempting to
protect capital and temper market volatility
Diversified hedge funds
Real Assets: Support the purchasing power of assets
Real estate
TIPS
Commodities
Natural Resource Equities
Economic Scenarios What Roles do Asset Classes Play?
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0% 5% 10% 15% 20% 25% 30% 35%
2%
4%
6%
8%
10%
Optimization Set: 2011 ESB PvtAsset Class Risk-Reward
Expected Volatility
ExpectedR
eturn
Capital Growth
Private Equity
Income
Real Assets
Real Estate
Volatility Hedge
Cash
Economic Scenarios Working Within the Mean Variance Optimizer
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Economic Scenarios The Building Blocks
0% 5% 10% 15% 20% 25% 30% 35%
2%
4%
6%
8%
10%
Optimization Set: 2011 ESB PvtAsset Class Risk-Reward
Expected Volatility
ExpectedR
eturn
Broad Domestic EquityGlobal Ex-US Equity
Private Equity
Domestic Fixed
High Yield
Non US FixedTIPS
Commodities
Natural Resources
Real Estate
REITsVolatility Hedge
Cash
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Factor Asset Class
Target
Al lo cation CapitalA
ppreciati
on
InflationProtection
Deflation/Crisi
s
Protection
CapitalP
reservati
on
Purcha
sing
Power
Preservation
IncomeGen
eratio
n
Diversific
ation
Alpha
Cash 2% Interest Rates 6%
U.S. Government Bonds 4% International Government Bonds 2%
Company Exposure 53%
Global Credit 11% Global Equity 36% Private Equity 6%
Real Assets 18%
Real Estate 12% Infrastructure 3% TIPS 3%
Special Opportunities 21%
Absolute Return 6% Real Return 7% Distressed Debt 1% Mezzanine Debt 1% Structured Credit 1% Other 5%
This is how one large plan combines factors and asset classes:
Source: Adapted from the Alaska Permanent Fund Corporations Investment Policy Statement dated 12/1/2010.
Economic Scenarios A Hybrid Example
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Practitioners will place increased emphasis on understanding how differentportfolios react to specific economic and capital market outcomes.
Asset classes will be increasingly defined by their expected reactions to thesedifferent economic environments.
Liquidity will be an explicit consideration both in strategic policy developmentand implementation.
Next Steps
in Asset AllocationWhat Does the Future Hold?
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Building portfolios of factors is challenging, but using factors to understandtraditionally constructed portfolios is very useful.
Limitations, including basic investability, preclude most market participantsfrom solely investing along factor lines.
However, factors provide a useful way to group traditional asset classesin macroeconomic buckets.
Several multi asset class managers can engineer strategies with specificfactor exposures; these can be used to augment existing frameworks.
Some factor exposures are explicitly incorporated within manager structureanalysis (such as liquidity, leverage, duration, currency, size, momentum,
etc.).
Conclusion