In either case, any single asset class benchmark is designed to identify the aggregate performance of the class and allow for standardized comparisons between and among market participants and managers within that class. Advisers can then measure the implementation skill, or “alpha,” behind a specific investment assignment.

However, a multi-asset class benchmark is generally designed to measure something else—the total alpha of all such investment assignments plus the alpha, if any, of a policy portfolio itself.

What is a policy portfolio in a multi-asset class context? It is the mix of asset classes and weights derived from the set of short- and long-term investment objectives and capital market assumptions for an investor or investment plan. This mix may change over time in certain ways to reflect changing objectives (e.g., liabilities) or assumptions (an attempt to capture excess returns from short-term asset class price fluctuations in (market timing).2

Establishing a policy portfolio is not an easy task. Numerous challenges and complexities arise almost immediately.

• How are these decisions made in practice?
• How are specific asset classes included?
• How are the weights set?
• Where do the capital market assumptions come from?
• How do these decisions evolve over time?

For example, multi-asset class efficient frontiers are based on, and highly sensitive to, their inputs and may contain volatile or inaccurate data (private equity and venture capital performance data come to mind), making it difficult to gain agreement on what that efficient frontier looks like. This is further complicated by the fact that there is no standing consensus on the asset-class palette; after all, the simple decision to invest strictly in domestic equities (or any other set of asset classes) creates the opportunity cost of not investing in some other market, such as international equities, commodities, real estate or bonds, to name just a few.

Establishing a policy portfolio is not a trivial exercise either. Analysis shows that the asset allocation decision explains about 90 percent of the variability of a fund’s returns over time, and on average across funds, explains approximately 100 percent of the level of returns.3 The constraints and decisions that drive the policy portfolio, whatever they may be, clearly have a big impact and must be evaluated appropriately.

Of course, an investor (and their adviser or consultant) must construct a policy portfolio even in the absence of specific guidance on these matters. After all, establishing an investment policy, delineating responsibility and measuring the performance contribution of those activities form the core of the investment management process.2

Yet benchmark identification is routinely dismissed as a lesser element of the process; since there is no single definitive approach, the ultimate decision is viewed as relatively inconsequential.

This perception is far from accurate. The need for benchmarking is heightened, not diminished, in a multi-asset class environment given the much wider spectrum of required decision making and investment opportunities.

The Case Of Target Date

In recent years, the individual retirement market has witnessed an explosion of “target date” products. These are multi-asset class products that dynamically and automatically evolve an investor’s “policy portfolio”—their asset class mix and weights—at each stage of an investor’s working life.

The benchmark process in this realm is very challenging. How should such evolving policy portfolios be measured against the finite horizons of individual investors who have real-world and often conflicting needs that may vary with age, existing wealth, living expenses and personal circumstances?

Given existing tools and capabilities, there may be a reasonable approach to benchmarking. In general, the proper design of a portfolio involves at least four decisions 2: (1) the asset-class palette; (2) strategic asset allocations; (3) tactical asset allocations (market timing); and (4) security selection. The first two decisions form the core of the policy portfolio, and should be the focus of benchmarking in a multi-asset class environment.

Ultimately, though, there will be ongoing debate about the objective nature and best practices of benchmarking such multi-asset class portfolios. The different approaches will each bring their own strengths and weakness, but the growing competition can only help benefit the market drive toward the optimal approach.

Jeffrey Fernandez, project manager, Analytics & Research, Dow Jones Indexes, contributed to this article.

References

1 Bailey, Jeffery V., “Are Manager Universes Acceptable Performance Benchmarks?” Journal of Performance Management, Spring 1992.

2 Brinson, Gary P., L. Randolph Hood, and Gilbert L. Beebower, “Determinants of Portfolio Performance,” Financial Analysts Journal, July/August 1986.
http://www.cfapubs.org/doi/pdfplus/10.2469/faj.v51.n1.1869

3 Ibbotson, Roger G. and Paul D. Kaplan, “Does Asset Allocation Policy Explain 40, 90, or 100 Percent of Performance?” Financial Analysts Journal, January/February 2000. http://corporate.morningstar.com/ib/documents/MethodologyDocuments/IBBAssociates/AssetAllocationExplain.pdf

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