Lecturer: Dr. Hua Deng
ANU Research School of Finance, Actuarial Studies and Statistics
Lecture 5
Asset Allocation: More Methods
Alpha & Beta
FINM 3008/8016
Applied Portfolio Construction
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Today’s lecture
What you can expect to learn:
• Some more methods to assist in making asset
allocation decisions
• About the taxonomy of alpha and beta
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Some asset allocation methods
• Mean-variance analysis
• Benchmark-relative
• Two-stage approach (Russell Investment)
• Dynamic strategies (Ibbotson Associates)
• Scenario analysis (Gosling, JPM, Fall 2010)
• Liability-driven investing
• Fundamental risk approach (Russell Investment)
• Factor investing (JP Morgan)
• Hierarchical clustering multi-asset multi-factor asset allocation
(Invesco)
Message: They are just models! Apply multiple methods to get
more robust results and exercise your judgment!
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Theoretical issues with traditional MPT
Issue Problem
1. Investment horizon is undefined Yet it matters, especially if returns are
not iid (identically and independently distributed)
2. Single period model Real world is multi-period, with
stochastically changing investment
opportunities
3. Assumes risk aversion is only
investor difference that matters
(‘separation’ => investors hold
combination of M and Rf)
Other investor differences matter,
e.g. objectives, liabilities, investment
horizon, opportunities, costs, taxes
(=> separation unlikely to hold in practice,
i.e. different portfolios may be optimal)
4. Portfolio optimization across all
available assets
Not necessarily feasible (curse of
dimensionality); multi-factor models
may be more effective
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Benchmark-Relative
• Estimate tracking error: TE = Std Dev(rPortfolio – rBenchmark)
– Data-based estimates: create and analyze two time series
– Parametric-based: analyze portfolio defined by wi,Portfolio – w
i,Benchmark
• Simplest approach is to impose a TE constraint
• Chow (FAJ, 1995) suggests this objective function:
• TP – values of 0.50 to 1.00 typically used in industry
• TTE – values of 0.10 to 0.50 have been suggested

TE
TE
P
P
P TT
rEUtility
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
E[rP] expected return for portfolio
σP
2 portfolio variance
σTE
2 portfolio tracking error variance
TP portfolio risk tolerance
TTE tracking error risk tolerance

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Two stage approach
• Setting the stage: collect inputs (goals,
preferences, circumstances, capital markets)
• Stage 1: decide broad asset class exposures
– Optimization is safe
– Use equal expected returns for major equity markets
– Resulting AA will meet plan risk-return objectives
– Evaluate against client-specific objectives
• Stage 2: decide performance enhancing
exposures
– Optimization is unsafe
– Rely on good judgment: supportable investment beliefs, logic,
experience, simulations and sensitivity analysis
– Evaluate against client-specific liabilities
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Dynamic strategies: Lifetime asset
allocation
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Dynamic strategies: typical lifecycle of
human capital and financial capital
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Scenario analysis
• Defining the individual scenarios: economic
growth, inflation, investor sentiment
• Generating return assumptions
• Assigning scenario probabilities
• Generating scenario output: provide rich
information of return distribution, risk and
diversification
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Liability-Driven Investing (LDI)
• Liability can be viewed as a negative asset
• Difference: it is not usually a choice variable
• The trade-off: Surplus risk versus expected return (or cost)
Surplus (Deficit) = Assets – Liabilities
Funding Ratio = Assets / Liability
• One approach: (more detail on next slide)
a) Identify minimum risk portfolio, i.e. best liability hedge
b) Find preferred position – the final asset allocation decision is
always linked to stakeholder’s objectives, i.e. the sponsoring entity
may push to increase asset risk for higher returns, hence less
contribution is needed
• Another approach is based around cash flow matching 11
Source: Submission to the Financial System Inquiry”, RBA, March 2014
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What drives DB liabilities
1. Discount rate applied to projected benefits
– approaches vary across jurisdictions
– often tied to bond yields, e.g. Aa corporate, treasury bonds
– expected return on assets used in US public sector
2. Drivers of benefit projections
– salary growth
– turnover of beneficiaries
– longevity (where full-life pension)
– options offered
3. Inflation – where it has differential effects on discount rate
and benefit projections
4. Other – accounting and regulation (eg AASB119)
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VLiability = Benefits
(1+r)n
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Wage, years of service, etc.
Discount rate = real
interest rate + inflation
Implementing LDI
1. Identify the measure of liability value (and hence surplus)
– For a DB fund, this might be the actuarial valuation (NPV) of future
benefit payments, or Projected Benefit Obligation (PBO)
2. Characterize how liability value relates to asset values:
– Mean-variance framework: covariance with assets
– Duration-matching (see Siegel & Waring, FAJ, 2004)
– Economic or factor-based
3. Locate the minimum risk portfolio
4. Characterize the trade-off (return vs surplus volatility / shortfall)
5. Choose preferred portfolio, given objectives & preferences
(all stakeholders have a say in this decision!)
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Fundamental Risk Approach
• Economic diversification
• A way of thinking about portfolios, and how they might
be improved at the margin:
• Step 1: Identify the common, fundamental risks to
which the overall portfolio is most exposed
• Step 2: Consider how the portfolio could be modified to
reduce risk exposure without sacrificing too much E[r]
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Some fundamental risks
(Revisiting Lecture 2)
• Macroeconomic
– Economy
– Income-share shifts
– Inflation
• Illiquidity
• Structural / systemic e.g. financial
crisis, political, demographic
• Home bias
Risk #2:
Income-Shares
Multiples,
cap rates,
discount
rates, etc
Risk #1:
Economy
Wages
Housing
Profits
Govt / tax
↓ Other ↓
Interest
Typical
portfolio
exposed
to asset
subset
Asset
Prices
and
Returns
Portfolio
Outcome
Total
Income
Risk #3:
Valuation of
Incomes
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Portfolio modifications
Attaining More Efficient Portfolios


Borrowing
Rate
Expected
Return

Risk Exposure
A D

C
’

Swapping for asset with lower return,
but much lower exposure to a risk
factor, gets from portfolio A to C
Leveraging portfolio
C gets to portfolio D
Panel B: Using Leverage
Expected
Return

Risk Exposure
A B

Swapping assets of different
exposure to a risk factor, but
similar expected return, gets
from portfolio A to B
Panel A: Basic Switch
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Factor Investing – Background
• Building portfolios from asset classes are easy to observe
and directly investable. However fundamental drivers of
risk and return are overlooked and ignored (eg. traditional
asset allocation approaches failed to deliver effective diversification
during GFC, many asset classes moved in lockstep, prompting
underlying common risk factors)
• Expected to enhance AA by highlighting portfolio-level
sensitivities and improve the risk-return trade-off for
investor’s total portfolio
• Factors can be identified using Principal Components
Analysis, regressions, etc.
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Asset Allocation of Institutional Funds
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Macro Factor View
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Characterizing Asset Classes with Factors
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Factor Investing – Industry Applications
• Risk factor optimization
• Robust Mean-Variance Optimization , eg. select the
portfolio that performs well in worst-case scenario
• Risk management
• Smart beta strategies
• Mimicking portfolios
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Hierarchical clustering multi-asset multi-
factor asset allocation approach
• Portfolio should be
diversified across
uncorrelated risk sources
• Principal Component
Analysis, advanced with
unsupervised machine
learning algorithm to
incorporate hierarchical
structure in asset
correlations: focus on
diversification of correlations
that matter
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Note: Covariance matrix estimated with on 5-year rolling window of month returns, dendrogram updated
monthly, portfolio rebalanced monthly.
DRP (Diversified Risk Parity), HRP (Hierarchical Risk Parity), HRP Smooth (HRP penalized for turnover)
Alpha & Beta
Beta Alpha
Theoretical
Definition
Returns attributed to
systematic risk factor,
undiversifiable
Returns that cannot
be explained by these
systematic risk
factors, idiosyncratic
Industry Definition Returns from passive
market exposure
Returns generated by
active management
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Alpha & Beta
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Beta is: Alpha is:
Widely available Relatively rare (and often
considered a ‘zero sum game’)
Should be cheap Valuable, and hence should be
more expensive
Associated with risk premia
/passive market exposure
Typically associated with
investment skill
Alpha & Beta: Conceptual Frameworks
1. Financial economics
– Focus on ‘ priced risk factors’
– Examples: CAPM; Fama-French 3-factor model
2. Performance attribution/evaluation
– Beta: widely available, comes from priced risk factors or persistent
market inefficiencies
– Alpha: relates to skill, hence should be identified and rewarded
3. Risk modeling
– Beta as common sources of variation (alpha is idiosyncratic)
– Beta exposures should be identified and managed
4. Exposure replication and hedging
– Beta can be replicated and/or hedged using vehicles such as index
futures, ETFs, swaps, etc
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Alpha & Beta - Issues
• Beta is not unique
• Market-timing = time-varying beta = alpha?
• Beta masquerading as alpha
“alpha (is often) just beta waiting to be discovered”
– AQR
• Alpha and beta cannot always be unbundled, e.g.
many alternative assets
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Alpha & Beta - Industry Applications
1. ‘Portable alpha’ (disappearing, hot 20 years ago)
− Ask a manager to beat a benchmark
− Short the benchmark using derivatives, what’s left is portable alpha
− Facilitates separation of the management of beta (asset class
exposure, etc) from alpha-seeking activities
− Implementation can be tricky and/or costly
2. ‘Smart beta’ (the latest fad)
− Other forms of indexation or mechanical strategies, other than the
standard market-capitalization weighting
− More transparent and less expensive than traditional actively
managed products, but more expensive than passive products
− Examples: fundamental indexation, minimum variance, factor-
mimicking (size / value / momentum)
− Black Rock iShares estimate: global smart beta ETF assets to reach $1
trillion by 2020, and $2.4 trillion by 2025 33
Fill the buckets
Alpha
Benchmark
Concentrated equity
Long/short equity
strategies
Pure alpha bond
strategies
GTAA
Market neutral equity
Pure alpha currency
Equity Fixed Income Alternatives Opportunistic
Australian equity
Global equity
Emerging market equities
Global REITs
US small-cap
EAFE small-cap
Global fixed income
High yield
Enhanced cash
Diversified income
Global credit
Inflation-linked debt
Emerging market debt
Private equity
Timberland
Diversified core real
estate
Core infrastructure
Real estate
Fund of hedge funds
Collateralised commodity
futures
Catastrophe bonds
Weather derivatives
Absolute return strategies
Distressed debt
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Alpha & Beta - Industry Applications
3. ‘Exotic beta’
Exotic beta (Litterman) – transitory excess returns tied to
a specific market-based exposure
− Temporary market mis-pricing or inefficiency
− Positive expected return, low correlation with global equity
market
− Possible exotic beta sources: high yield bonds, catastrophe
bonds, commodities, emerging market equity, emerging market
bond, global real estate, global small cap
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Portfolios should contain a complete
spectrum of return sources?
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Wrap-up
• Final messages
• Comments on tutorial
• Comments on readings
– Examinable readings: focus on the concepts covered in lecture,
you may go deeper in the numerical examples if you like
– Supplementary readings on asset allocation approaches: build
up the knowledge base for your assignment or future reference.
You should have some ideas what methods have been
developed and choose the appropriate ones to your needs.
– Supplementary readings on smart beta: intend to show you
various aspects of this strategy, to round up your understanding
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