Refresher Reading
Principles of Asset Allocation
2024 Curriculum CFA Program Level III Portfolio Management and Wealth Planning
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Determining a strategic asset allocation is arguably the most important aspect of the investment process. This reading builds on the “Introduction to Asset Allocation” reading and focuses on several of the primary frameworks for developing an asset allocation, including asset-only mean–variance optimization, various liability-relative asset allocation techniques, and goals-based investing. Additionally, it touches on various other asset allocation techniques used by practitioners, as well as important related topics, such as rebalancing.
The process of creating a diversified, multi-asset class portfolio typically involves two separate steps. The first step is the asset allocation decision, which can refer to both the process and the result of determining long-term (strategic) exposures to the available asset classes (or risk factors) that make up the investor’s opportunity set. Asset allocation is the first and primary step in translating the client’s circumstances, objectives, and constraints into an appropriate portfolio (or, for some approaches, multiple portfolios) for achieving the client’s goals within the client’s tolerance for risk. The second step in creating a diversified, multi-asset-class portfolio involves implementation decisions that determine the specific investments (individual securities, pooled investment vehicles, and separate accounts) that will be used to implement the targeted allocations.
Although it is possible to carry out the asset allocation process and the implementation process simultaneously, in practice, these two steps are often separated for two reasons. First, the frameworks for simultaneously determining an asset allocation and its implementation are often complex. Second, in practice, many investors prefer to revisit their strategic asset allocation policy somewhat infrequently (e.g., annually or less frequently) in a dedicated asset allocation study, while most of these same investors prefer to revisit/monitor implementation vehicles (actual investments) far more frequently (e.g., monthly or quarterly).
Section 2 covers the traditional mean–variance optimization (MVO) approach to asset allocation. We apply this approach in what is referred to as an “asset-only” setting, in which the goal is to create the most efficient mixes of asset classes in the absence of any liabilities. We highlight key criticisms of mean–variance optimization and methods used to address them. This section also covers risk budgeting in relation to asset allocation, factor-based asset allocation, and asset allocation with illiquid assets. The observation that almost all portfolios exist to help pay for what can be characterized as a “liability” leads to the next subject.
Section 3 introduces liability-relative asset allocation—including a straightforward extension of mean–variance optimization known as surplus optimization. Surplus optimization is an economic balance sheet approach extended to the liability side of the balance sheet that finds the most efficient asset class mixes in the presence of liabilities. Liability-relative optimization is simultaneously concerned with the return of the assets, the change in value of the liabilities, and how assets and liabilities interact to determine the overall value or health of the total portfolio.
Section 4 covers an increasingly popular approach to asset allocation called goals-based asset allocation. Conceptually, goals-based approaches are similar to liability-relative asset allocation in viewing risk in relation to specific needs or objectives associated with different time horizons and degrees of urgency.
Section 5 introduces some informal (heuristic) ways that asset allocations have been determined and other approaches to asset allocation that emphasize specific objectives.
Section 6 addresses the factors affecting choices that are made in developing specific policies relating to rebalancing to the strategic asset allocation. Factors discussed include transaction costs, correlations, volatility, and risk aversion.
Section 7 summarizes important points and concludes the reading.
Learning Outcomes
The member should be able to:
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describe and critique the use of mean–variance optimization in asset allocation;
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recommend and justify an asset allocation using mean–variance optimization;
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interpret and critique an asset allocation in relation to an investor’s economic balance sheet;
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discuss asset class liquidity considerations in asset allocation;
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explain absolute and relative risk budgets and their use in determining and implementing an asset allocation;
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describe how client needs and preferences regarding investment risks can be incorporated into asset allocation;
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discuss the use of Monte Carlo simulation and scenario analysis to evaluate the robustness of an asset allocation;
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describe the use of investment factors in constructing and analyzing an asset allocation;
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recommend and justify an asset allocation based on the global market portfolio;
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describe and evaluate characteristics of liabilities that are relevant to asset allocation;
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discuss approaches to liability-relative asset allocation;
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recommend and justify a liability-relative asset allocation;
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recommend and justify an asset allocation using a goals-based approach;
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describe and critique heuristic and other approaches to asset allocation;
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discuss factors affecting rebalancing policy.
Conclusions
This reading has surveyed how appropriate asset allocations can be determined to meet the needs of a variety of investors. Among the major points made have been the following:
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The objective function of asset-only mean–variance optimization is to maximize the expected return of the asset mix minus a penalty that depends on risk aversion and the expected variance of the asset mix.
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Criticisms of MVO include the following:
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The outputs (asset allocations) are highly sensitive to small changes in the inputs.
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The asset allocations are highly concentrated in a subset of the available asset classes.
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Investors are often concerned with characteristics of asset class returns such as skewness and kurtosis that are not accounted for in MVO.
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While the asset allocations may appear diversified across assets, the sources of risk may not be diversified.
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MVO allocations may have no direct connection to the factors affecting any liability or consumption streams.
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MVO is a single-period framework that tends to ignore trading/rebalancing costs and taxes.
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Deriving expected returns by reverse optimization or by reverse optimization tilted toward an investor’s views on asset returns (the Black–Litterman model) is one means of addressing the tendency of MVO to produce efficient portfolios that are not well diversified.
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Placing constraints on asset class weights to prevent extremely concentrated portfolios and resampling inputs are other ways of addressing the same concern.
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For some relatively illiquid asset classes, a satisfactory proxy may not be available; including such asset classes in the optimization may therefore be problematic.
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Risk budgeting is a means of making optimal use of risk in the pursuit of return. A risk budget is optimal when the ratio of excess return to marginal contribution to total risk is the same for all assets in the portfolio.
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Characteristics of liabilities that affect asset allocation in liability-relative asset allocation include the following:
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Fixed versus contingent cash flows
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Legal versus quasi-liabilities
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Duration and convexity of liability cash flows
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Value of liabilities as compared with the size of the sponsoring organization
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Factors driving future liability cash flows (inflation, economic conditions, interest rates, risk premium)
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Timing considerations, such longevity risk
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Regulations affecting liability cash flow calculations
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Approaches to liability-relative asset allocation include surplus optimization, a hedging/return-seeking portfolios approach, and an integrated asset–liability approach.
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Surplus optimization involves MVO applied to surplus returns.
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A hedging/return-seeking portfolios approach assigns assets to one of two portfolios. The objective of the hedging portfolio is to hedge the investor’s liability stream. Any remaining funds are invested in the return-seeking portfolio.
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An integrated asset–liability approach integrates and jointly optimizes asset and liability decisions.
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A goals-based asset allocation process combines into an overall portfolio a number of sub-portfolios, each of which is designed to fund an individual goal with its own time horizon and required probability of success.
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In the implementation, there are two fundamental parts to the asset allocation process. The first centers on the creation of portfolio modules, while the second relates to the identification of client goals and the matching of these goals to the appropriate sub-portfolios to which suitable levels of capital are allocated.
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Other approaches to asset allocation include “120 minus your age,” 60/40 stocks/bonds, the endowment model, risk parity, and the 1/N rule.
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Disciplined rebalancing has tended to reduce risk while incrementally adding to returns. Interpretations of this empirical finding include that rebalancing earns a diversification return, that rebalancing earns a return from being short volatility, and that rebalancing earns a return to supplying liquidity to the market.
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Factors positively related to optimal corridor width include transaction costs, risk tolerance, and an asset class’s correlation with the rest of the portfolio. The higher the correlation, the wider the optimal corridor, because when asset classes move in sync, further divergence from target weights is less likely.
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The volatility of the rest of the portfolio (outside of the asset class under consideration) is inversely related to optimal corridor width.
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An asset class’s own volatility involves a trade-off between transaction costs and risk control. The width of the optimal tolerance band increases with transaction costs for volatility-based rebalancing.