On Investment Objectives and Risks, Clear Communication Is Key, Part 2

Adapted by Lisa M. Laird, CFA, from “Communicating Clearly about Investment Objectives and Risks” by Karyn Williams, PhD, and Harvey D. Shapiro, originally published in the July/August 2021 issue of Investment and wealth monitor.1

In the first article in this series, we discussed the need for clear communication at the initial stage of the investment process. We started with purpose and goals as the basis for basic investment strategy decisions. In this second installment, we identify the communication challenges that accompany traditional investment decision frameworks and risk concepts such as standard deviation.

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So what about traditional investment decision frameworks?

Most major institutional investors hire consultants to help the parties involved communicate and evaluate the trade-off between risk and return. Most use a mean-variance optimization (MVO) framework to help investors make these decisions.2 In an MVO framework, the target return is the “mean” or reward of a portfolio, and the standard deviation is the “variance” or risk. MVO makes the investment strategy decision simple and elegant: each target return corresponds to an “efficient portfolio” with a risk that is defined by one standard deviation.

But standard deviation cannot characterize risk in a meaningful way for most investors. It measures the variation in portfolio returns, up and down. But most investors don’t see rising portfolio values ​​as a risk: they care about losing money. They often think of returns in absolute terms, and tend to agree with the adage that you can’t eat relative returns, meaning returns relative to a benchmark. And while many investors recognize that they can face a decline in portfolio value, especially in any type of crisis, the main risk in their eyes is avoiding whatever they consider their maximum allowable loss, also known as risk capacity or “loss”. limit.”

Only by chance would an investor’s loss limit ever equal the standard deviation of an MVO portfolio. The graph below shows a mean-variance frontier, with the highest expected target returns and corresponding standard deviations for two portfolios. For the public foundation with a target return of 6.75%, the standard deviation of the mean-variance efficient portfolio is about 13%. In practice, an advisor could translate a 13% standard deviation into a loss level that has a 5% chance of occurring, or roughly 1.65 standard deviations, which in this case is 15%. But what if the investor’s loss limit is 10%? And if it is 25%? And what if 5% is too high or low a chance to lose 10% or 25%?

Mean-variance efficient portfolios

Chart showing the performance of mean-variance efficient portfolios

If the loss limit is 10% and a 5% probability of such a loss is acceptable, the foundation’s mean-variance efficient portfolio has a standard deviation of approximately 9.7% and an expected return of less than 6% (−10% = 6% – 1.65). × 9.7%). This is a very different portfolio. Untranslated for the investor, the probability of reaching 6.75% for this lower risk portfolio is unknown. This makes tradeoffs with this framework difficult at best, especially for non-investment professionals.

In either case, the standard deviation turns out to be less than descriptive of the realistic potential portfolio outcomes and the potential paths to those outcomes, so MVO excludes critical decision information. Above all, it ignores the potential for very large declines in portfolio value (tail risk), smaller, sustained declines in portfolio value (sequence risk), and portfolio depletion (depletion risk). during an investment horizon.

Journal of financial analysts Current number mosaic

Tail risks come into play more often than MVO assumes.3 The chart below shows potential portfolio values ​​(outcomes) under non-normal and realistic asset return assumptions for a $100 million private foundation portfolio with a target return of 8.04%. The portfolio’s strategic asset allocation is 30% US equities, 30% non-US equities, 30% US fixed income and 10% hedge funds broadly diversified The five-year investment horizon results for both distribution assumptions reflect the foundation’s strategic allocation and investment activities over the five-year horizon, including quarterly expenses, fees and rebalancing of assets The averages of the results are indicated by the vertical lines.

Distributions of portfolio results, net of outflows and rebalancing

Chart showing distributions of portfolio results (net of exits and rebalancing)

The differences in results are significant, especially in terms of potential losses. Any decision that excludes this potential for loss may result in regret, forced selling, unexpected costs, lower-than-anticipated cumulative annual growth rates, and burnout.

The following table shows the common standard metrics used to describe portfolio risks for each resulting portfolio allocation. Decision makers face the challenge of interpreting these metrics. Assuming it is not normal, is 14% too high a standard deviation? What level of confidence is appropriate for value at risk (VaR)? In general, these standard metrics do not convey sufficient meaning because they lack context—the specific information that decision makers need to make informed decisions about risk.

Standard investment risk metrics

normal It’s not normal
Annualized standard deviation 10% 14%
Five-year value at risk (95th percentile) 29% 44%
Five-year conditional value at risk (95th percentile) 33% 51%
Average reduction 11% 13%
Average maximum reduction 21% 29%

Amid this disconnect between standard metrics and the investment context, institutions naturally prefer to make vague, if any, references to risk in their investment policies. They will offer statements such as: “Achieving 5% growth plus inflation and expenses over the investment horizon,” “Maximizing long-term returns consistent with prudent levels of risk,” “Achieving reasonable returns with levels of acceptable risk”. or “Achieving a performance above the policy benchmark by 2% over three-year periods.”

Cover image of the book Risk Tolerance and Circumstances

The bottom line is that an MVO approach is seriously deficient when it comes to risk, and standard metrics are meaningless. Most importantly, these metrics can lead to poor investment decisions and cause regret.

In the final article in this series, we’ll explore an alternative approach to enabling decision making between competing goals.


1. Investment and wealth monitor is published by Investments & Wealth Institute®.

2. The MVO framework finds the maximum expected return corresponding to a given level of portfolio risk. Risk is typically defined as the volatility of a portfolio of assets. The framework is based on Harry Markowitz’s founding 1952 paper.

3. Financial market data show non-normal behavior such as volatility clustering, autoregression, fat tails, skewness, and asymmetric dependencies. For a summary of the stylized facts describing price changes and their impact on securities, asset classes, and portfolios, see “Many Risks, One (Optimal) Portfolio,” by Cristian Homescu.

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All posts are the opinion of the author. Therefore, they should not be construed as investment advice, nor do the views expressed necessarily reflect the views of the CFA Institute or the author’s employer.

Image credit: ©Getty Images / aluxum

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Lisa M. Laird, CFA

Lisa M. Laird, CFA, is a Principal and Senior Advisor at Hightree Advisors, LLC. She is a Trustee of the Foundation and is a former Chief Investment Officer, Investment Committee Member, Board Member and Investment Consultant. Contact her at [email protected]

Harvey D. Shapiro

Harvey D. Shapiro is a senior advisor at Institutional Investor, Inc., where he has been a senior contributing editor for Institutional Investor magazine, as well as an advisor and moderator for numerous Institutional Investor conferences. A former adjunct professor and Walter Bagehot Fellow at Columbia University, he has been a consultant to several foundations and other institutional investors. He earned degrees from the University of Wisconsin, Princeton University and the University of Chicago. Contact him at [email protected]

Karyn Williams, PhD

Karyn Williams, PhD, is the founder of Hightree Advisors, LLC, an independent provider of investment decision tools, success metrics and risk information. She is a director of investments, patron of the foundation, director of an independent public company and former investment consultant. He earned a BA in economics and a PhD in finance, both from Arizona State University. Contact her at [email protected]

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