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 1 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 2 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! 3 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 4 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 22 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 5 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 6 Dynamic strategies: Lifetime asset allocation 7 Dynamic strategies: typical lifecycle of human capital and financial capital 8 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 9 10 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 12 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) 13 VLiability = Benefits (1+r)n 14 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!) 15 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] 16 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 17 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 18 19 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. 20 Asset Allocation of Institutional Funds 21 Macro Factor View 22 Characterizing Asset Classes with Factors 23 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 24 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 25 26 27 28 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 29 Alpha & Beta 30 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 31 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 32 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 34 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 35 Portfolios should contain a complete spectrum of return sources 36 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 37
