This article is part of our Retirement Plan Trends series, which explores issues affecting the retirement space.
Since the advent of modern portfolio theory more than half a century ago,1 investors and industry practitioners have concentrated on risk and return above nearly everything else.
The prescription is simple: pursue investments that offer the highest reward for a given level of risk (commonly expressed as a measure of volatility) or, conversely, that incur the lowest risk for a given level of return. Within this paradigm, metrics such as the Sharpe ratio2 and information ratio3 have become common tools for constructing asset allocations and evaluating portfolio performance. At a high level, they distill return and risk into a single number.
But such metrics share an important limitation: They are inherently two-dimensional.
Investors are much more complex with multiple — often competing — needs. These needs typically evolve over time. Risk and return are important, but so are things like income, inflation protection and preservation of capital during times of market stress. Focusing too narrowly on traditional optimization metrics, using just two dimensions, might mean missing what matters most to investors.
Recognizing that one size does not fit all, investment managers have sought to refine their asset allocation approaches to incorporate a broader perspective. One critique of traditional optimization approaches is their sensitivity to input estimations, which can dramatically impact an investor’s portfolio. Some asset managers have sought to address this and other limitations by applying a strong understanding of investor needs within multi-objective optimization models.
Multi-objective optimization examines multiple potential portfolios along multiple dimensions, as represented by various quantitative success metrics. An optimized portfolio can collectively deliver the most exposure to desired success metrics, which, in turn, can maximize exposure to the various real-world objectives associated with those metrics. For example, for a retiree, this optimization could incorporate market drawdowns or inflation shocks, in addition to expected return and volatility.
We believe that by taking a broader view of an investor’s goals, multi-objective optimization can offer managers of multi-asset portfolio solutions, such as target date strategies, a more nuanced approach to portfolio construction that can better address participants’ diverse objectives. Our approach combines deep research into investor personas, simulation modeling and robust quantitative methods to inform portfolio design. This is one element of our overall asset allocation process in our multi-asset solutions including our target date retirement strategy.
In the paper linked below, we take a closer look at the multi-objective optimization approach.
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1 Markowitz, Harry. “Portfolio Selection,” The Journal of Finance, March 1952.
2 Sharpe ratio is calculated by using standard deviation and excess return to determine reward per unit of risk. The higher the Sharpe ratio, the better the portfolio’s historical risk-adjusted performance.
3 Information ratio measures portfolio returns beyond the returns of a benchmark (or index) compared to the volatility of those returns.
Although the target date portfolios are managed for investors on a projected retirement date time frame, the allocation strategy does not guarantee that investors' retirement goals will be met. Investment professionals manage the portfolio, moving it from a more growth-oriented strategy to a more income-oriented focus as the target date gets closer. The target date is the year that corresponds roughly to the year in which an investor is assumed to retire and begin taking withdrawals. Investment professionals continue to manage each portfolio for approximately 30 years after it reaches its target date.