Portfolio Construction

Think beyond risk vs. return: Enhancing portfolio construction


    • Conventional portfolio construction methods often focus on fine-tuning narrowly defined measures of risk and return, which might not fit all investors
    • At any given point, investors typically have several competing needs and goals with relative importance that evolves over time
    • Quantitative techniques that optimize portfolios for multiple objectives can potentially improve the investment experience when used along with research into investor behavior and qualitative judgment

    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 ratio 3 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 — and evolving needs. Risk and return are important, but so are things like income, inflation protection and preservation of capital during times of market stress. In addition, the relative importance of these objectives typically changes over the course of an investor’s lifetime. Focusing too narrowly on traditional optimization metrics, using just two dimensions, might mean missing what matters most to investors. 

    Beginning from an origin point, a vertical arrow extends upward to form the y-axis, labeled “Return,” and a horizontal arrow extends to the right to form the x-axis, labeled “Risk,” of a typical scatter plot. Two points, labeled “Portfolio B” and to its upper left, “Portfolio A,” are depicted by dots colored dark and light blue. A series of smaller arrows extending up and to the left toward Portfolio A appear and flash in sequence, alluding to the objective of maximizing return while minimizing risk. The dots and axis labels disappear and the y-axis sweeps counterclockwise to become a horizontal line extending leftward from the origin. Together with the initial x-axis, they now form the diameter of the largest of several concentric semicircles labeled “Greater exposure to objective” and scaled from 0% to 100%. Each radial arc, or “slice,” of the pie corresponds to a different investor objective. From the left, “Capital appreciation” and “Equity income” are together shaded in blue representing “Equity objectives.” Moving toward the center, the fixed income objectives, shaded in green, begin with “Return-seeking income,” “Income for withdrawal” and “Inflation protection.” Moving clockwise, the final two objectives are “Diversification from equity,” and “Capital preservation.” Two angular shapes appear, representing optimized Portfolio A and Portfolio B. Both simultaneously expand, in order of the prominence of the objectives, toward the capital appreciation, equity income, diversification from equity. and inflation protection arcs. In each case, optimized Portfolio A has greater surface area than Portfolio B.

    Source: Capital Group. This is a hypothetical example for illustrative purposes only, not intended to portray an actual investment.

    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. Compared to a suboptimal portfolio, an optimized portfolio could, for example, have higher expected portfolio yield, higher expected return and lower expected volatility.


    We believe that by taking a broader view of an investor’s goals, multi-objective optimization can offer managers a more nuanced approach to portfolio construction that can better address 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 the paper linked below, we take a closer look at the multi-objective optimization approach. 

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    Raj Paramaguru is a solutions portfolio manager with 26 years of industry experience (as of 12/31/2023). He holds an MBA with honors from University of Chicago’s Booth School of Business, a master’s degree in industrial engineering from the University of Cincinnati and a bachelor’s degree in mechanical engineering from Anna University, India. He also holds the Chartered Financial Analyst® designation.

    Ilia Lanski is a solutions analyst with 23 years of industry experience (as of 12/31/22). He holds a PhD in theoretical physics from the Budker Institute of Nuclear Physics  and a master’s degree in physics from Novosibirsk State University, both in Russia.  

    1 Markowitz, Harry. “Portfolio Selection,” The Journal of Finance, March 1952.

    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.

    Information ratio measures portfolio returns beyond the returns of a benchmark (or index) compared to the volatility of those returns.

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