July 2025 Market Commentary

July 2025 Commentary

Our Views on Risk and Its Role in Wealth Management

With high levels of geopolitical risk and uncertainty around tariffs and their potential impact on global growth and inflation, many investors have seen significant changes in the level of risk in their portfolios in the last six months. Since the beginning of 2025, the Volatility Index (VIX) has been as high as 52.3 and as low as 12.5; even if an investor’s positioning has been stable, calculated levels of risk may have fluctuated significantly if shorter-term volatility and correlation were used as inputs. Of course, stable positioning has not been the norm year-to-date. Many portfolio managers have done a full round trip this year— starting the year aggressively positioned, de-risking after tariff announcements and then putting risk back on as markets rallied.

This concept is important: Even if a portfolio’s holdings haven’t changed, the portfolio’s risk can drift simply because the overall market environment has become more or less volatile. To complicate matters, while volatility is fluctuating, changes in asset allocation or security selection can also have a significant impact on portfolio risk. By “asset allocation,” we mean the percentage of the portfolio allocated to each major asset class like equities and bonds. By “security selection,” we mean the composition and amounts of specific holdings within each asset class. For example, do we hold Coke or Pepsi, both, or neither? If these factors aren’t managed effectively, investors’ long-term goals may be negatively impacted.

As Yogi Berra famously said, “A nickel ain’t worth a dime anymore.” Given the chaos in this year’s markets, it has occasionally felt like that. So, this month’s commentary examines the way we approach risk and the tools we use to ensure that client portfolios are appropriately positioned regardless of the market environment.

Initial Assessment of Risk Tolerance

Before an investor makes any decisions, the investor and manager need to understand the investor’s risk tolerance. To gain insight into their risk tolerance, wealth management investors often complete a risk assessment exercise in addition to extensive discussions with the manager to determine a “risk score” and what type of portfolio is most appropriate for their goals. The assessments are generally designed to identify the point in the risk and return tradeoff where the cost of any incremental potential gain exceeds the investor’s appetite for risk. The risk that we’re talking about is often described in two ways: in one case, risk can be described as a range of possible outcomes around the expected dollar gain. In the second case, risk can be described as a worst-case scenario given how the portfolio is invested to achieve the targeted gain.

The end result of the assessment might be a categorical score such as “Low Risk,” “Medium Risk,” or “High Risk.” On the other hand, it might be a numerical score that could range from 0 to 100, for example. In an investment world where volatilities and correlations are constantly changing, the risk score is an anchor. If an investor retakes the risk assessment and answers the questions identically, then their risk score should remain unchanged.

Once an investor has completed a risk assessment and has a risk number, the next step is to determine what strategy is consistent with their risk category or number. This strategy is often represented by a benchmark portfolio, whose long-term risk and return characteristics are well understood and consistent with the investors’ risk tolerance. That is, the risk category or number of the recommended strategies should be approximately equal to the investor’s risk category or number. As a practical matter, the universe of possible strategies is often segmented into a smaller set of “models” that span the return and risk characteristics over progressively riskier ranges of scores. The benchmarks for each model reflect weightings for each asset class that result in representative average long-term expected returns and volatilities.

The goal is to map investors into an appropriate model. While this mapping may sound complex, it can be relatively straightforward. Think back to the risk assessment and the factors that the investor was considering. Exhibit 1 shows selected risk assessment factors and their investment equivalents:

Exhibit 1. Correspondence Between Risk Assessment Factors and Investment Model Equivalents

Source: AAFMAA Wealth Management & Trust LLC

If we have the annualized expected return and volatility of a model’s benchmark portfolio, we can make an assumption about the distribution of the benchmark’s returns, then calculate an “expected shortfall” for the model, and, using that shortfall, assign a risk score. Let’s imagine a highly simplified world where there are only two asset classes (stocks and bonds) and three investment models that correspond to “Low Risk,” “Moderate Risk,” and “High Risk.” We’ll look at the expected shortfall at the 5% level, which is simply the average market loss of the worst 5% of outcomes. The 5% level is common and is consistent with metrics typically used to estimate the worst-case dollar loss in various risk assessment questionnaires. Our expected shortfall is assumed to occur over a one-year period, though the same methodology can be calibrated just as easily to shorter or longer periods.

Exhibit 2. How Investor Risk Assessments, Risk Scores, and Investment Models Are Related: An Example

Source: AAFMAA Wealth Management & Trust LLC

The process is illustrated in Exhibit 2. From left to right, the exhibit shows that an investor’s risk assessment exercise helps determine their comfort with potential market loss. That comfort level is translated into a risk score, and that risk score is mapped into an investment model whose risk score is similar. In this specific example, the risk assessment reveals that the investor is comfortable absorbing a market loss over the course of a year as high as 17%. That corresponds to a risk score of 52, which in our overly simple world would put the investor in the Moderate Risk Model.

We know that because the exhibit also shows, from right to left, that the historical performance of a set of moderately risky benchmarks corresponds in this example to expected shortfalls that range from 14% to 20% with equivalent risk scores that range from 31 to 73.

The same process that is used to assign a risk score to an investor is used to assign a risk score to a benchmark, a portfolio, or an investment model. However, whereas a benchmark, portfolio, or investment model’s score is based on long-term historical performance of the holdings, the starting point for an investor depends on their individual circumstances, including age, current wealth, family size, and expenses, among potentially many other considerations.

Exhibit 3 shows an example of how expected shortfalls can be mapped into a range of risk scores from 0 to 100 and into our simple three-model universe. As you can see, the mapping is continuous, so it’s relatively easy to make small changes in portfolio exposures such that you achieve a level of expected shortfall that maps into any target risk number.

Exhibit 3. An Example of a Possible Relationship Between Risk Scores and Expected Shortfalls

Source: Bloomberg, AAFMAA Wealth Management & Trust LLC

While most risk assessments are heavily focused on expected shortfall or other expected loss measures, we believe that a complete risk assessment would also provide the investor with an idea of the opportunity cost of being in a portfolio with lower risk. Exhibit 4 shows the cumulative growth of a $100,000 investment by constant allocation to equity over the 20-year period from 2005 – 2024. From left to right, the curve shows the final value of the investment after 20 years, assuming that in each month returns are reinvested at the given allocation to stocks, with the remainder invested in bonds. At the end of the 20-year period, a 100% allocation to the S&P 500 would have been worth $717,503, while a blend of 50% stocks/50% bonds would have been worth $380,672. The 100% equity portfolio was therefore worth almost twice as much as the 50/50 blend over the 20-year period. The opportunity cost is the additional amount of potential return an investor would have given up (in this case $717,503 - $380,672 = $336,831) to avoid incurring additional risk. We believe that it is important that investors understand opportunity cost as well as potential downside when doing a risk assessment.

Exhibit 4. Growth of $100,000 Invested in Stocks and Bonds By Allocation to Stocks Over the Period 2005 - 2024

Source: Bloomberg, AAFMAA Wealth Management & Trust LLC

The Model Benchmark Through Time

Remember that as long as the investor provides the same answers to the risk assessment, their risk number will not change. So, at initial funding, it’s possible that the investor’s risk score and the risk score of their model’s benchmark may be identical or very close to identical. Therefore, while the investor’s risk score remains stable – because it reflects the intrinsic circumstances of the investor regardless of the market’s ups and downs – the same is not true for the risk score of a portfolio that is identical to the model benchmark, precisely because a portfolio is affected by the market’s ups and downs. In fact, the risk score of any portfolio is a function of the expected returns and volatilities of each of its constituent assets and the correlations among those assets. The expected returns, volatilities, and correlations change over time, and thus, the risk score of any portfolio changes over time. For model selection purposes, the risk score of the benchmark is based only on the benchmark’s long-term expected return and volatility. That can lead to the confusing result that a portfolio whose holdings are weights identical to a model benchmark’s can have a different risk score than the benchmark.

Active Investment Management and Its Impact on Risk Scores

As we just noted, model benchmarks are designed to have appropriate risk and return characteristics over very long-time horizons (10 years or more). In the short run, a benchmark’s constituent holdings may be far from optimal in terms of expected return and risk. As a result, an active manager may (and likely will) tactically deviate from the benchmark to take advantage of opportunities or manage risk. Consequently, the risk score of the client’s portfolio may temporarily differ significantly from the model benchmark’s risk score and the client’s risk score. This will especially be true during periods of elevated uncertainty when the portfolio might be more defensive or during periods of above-average opportunity when the portfolio might hold higher risk. Because of the volatile nature of the risk score of any actively traded portfolio, a model’s current portfolio holdings should not be used in determining if that model is appropriate for an investor with a particular risk score; this is exclusively the role of the model benchmark.

How does the Active Investment Manager Scale Active Positions?

An active manager has two ways to deviate from the benchmark: using changes in asset allocation (how much of the portfolio is invested in equities, bonds, commodities, etc.) or changes in security selection (within each asset class, how much of, and which, securities are owned). In either case, when a benchmark is involved, it is generally important that the active manager restricts the size of active bets so that the short-term risk of underperforming the benchmark remains in an appropriate range for the product. To help the manager control that risk, tracking error risk limits are often set.

What is Tracking Error?

Tracking error is a measure of how different the returns of two portfolios are. If we have the historical returns of two portfolios, we can calculate the tracking error directly from the differential returns of each portfolio. Exhibit 5 shows monthly returns for the S&P 500 and a hypothetical equity portfolio. Technically, the tracking error between the two portfolios is simply the standard deviation of the difference in monthly returns. When the calculation is based on historical data, it is often referred to as “ex-post” tracking error because it measures how closely the equity portfolio tracked the S&P 500 in the past.

Exhibit 5. Calculation of Ex-Post Tracking Error

Source: Bloomberg, AAFMAA Wealth Management & Trust LLC

In this example, we see that the annualized tracking error is 3.84%. What does this mean? If the differential returns are normally distributed (this is a simplifying assumption used to more easily illustrate the concept), then 68% of the time, annualized portfolio return would equal the annualized S&P 500 return plus or minus 3.84%. Whether 3.84% is “good” depends on the portfolio objective. If the objective is to closely track the S&P 500, then a much lower tracking error would probably be appropriate. If the investor is willing to accept this amount of variability in order to add alpha (return above that of the S&P 500), then this might be an acceptable amount of tracking error. Tracking error limits need to be understood by both investors and managers to understand the range of potential portfolio return outcomes compared with the model benchmark.

In the previous example, we looked at ex-post or historical tracking errors. When a portfolio manager is trying to make risk decisions in real time, knowing ex-post tracking error is of little use. Fortunately, instead of actual differential returns, we can use deviations in position sizes between the two portfolios combined with estimates of correlation and volatility to estimate future tracking errors in real time. Exhibit 6 shows the calculated tracking error between a simple portfolio holding 80% S&P 500 and 20% Bloomberg Aggregate and a benchmark portfolio, which is 100% S&P 500. This calculation is often called “ex-ante” tracking error, as it is a measure of how much portfolio returns might differ from benchmark returns in the future.

Exhibit 6. Calculation of Ex-Ante Tracking Error

Source: Bloomberg, AAFMAA Wealth Management & Trust

Using a simple formula from basic investment management, we can use the differential weights, estimated volatility of each asset, and the estimated correlation between the two assets to calculate the ex-ante tracking error.

The Use of Bounds in Controlling Risk

You might look at Exhibit 6 and ask,  “If 3.11% is an acceptable level of tracking error, why not simply use the asset class allocation bounds as a means of controlling risk?" In this example, that might imply setting bounds for this portfolio such that the minimum equity exposure is 80% and the maximum fixed income exposure is 20%. Those bounds might keep your tracking error around or below 3.11% if only asset allocation changes were made. The problem is that we often have to take into account more than just the asset allocation decision. Remember, the active portfolio manager can deviate from the model benchmark in two ways: through adjustments to asset allocation or adjustments to security selection. Let’s take a look at a quick example to see how much tracking error can be generated by security selection alone.

In our example with a benchmark portfolio of 100% S&P 500, let’s assume that the investment manager wants to make the portfolio more defensive, but rather than reduce equity exposure, the manager simply skews the portfolio towards value and includes mid-cap companies as well as large-cap companies. If the manager were to replace securities from the S&P 500 with the Russell 1000 Value, the new portfolio would have 6.98% tracking error to the S&P 500! If the manager wanted to combine the defensive security selection with the previous defensive asset allocation decision (shift to 80% equity and 20% fixed income), the tracking error would move up to 7.86%. These decisions might all fall within allowable guidelines using limits based solely on asset class allocation bounds, but 7.86% tracking error is very close to the long-term average expected return of the S&P 500 and would risk an outcome that would be significantly different than the S&P 500. As you can see from these examples, security selection tracking error can be significant and may dominate the tracking error from asset allocation when constrained by asset allocation bounds; therefore, asset allocation bounds alone are not sufficient to properly control portfolio risk.

In April, when we reduced risk, we maintained the percentage allocation to equities and fixed income of our client portfolios but adjusted the security selection to achieve our goals.

Conclusion

Effective risk management is of critical importance in successful portfolio management. Risk management begins with determining the investor's risk tolerance. In placing an investor in the correct model, it’s important to consider potential loss or shortfall as well as the opportunity cost of investing in a less risky portfolio. To ensure that investors remain in the correct strategy given their risk score, benchmark holdings need to be monitored through time, as their risk scores will change as volatilities and correlations change. Finally, in the case of actively managed portfolios, we believe that portfolio managers should be using tracking error or a similar metric to ensure that a portfolio’s return deviation from the benchmark is within expectations.

We spend a significant amount of time focusing on portfolio risk to ensure our clients’ outcomes are consistent with their long-term goals.

Yours in trust,

Paul and Parker

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© 2025 AAFMAA Wealth Management & Trust LLC. Information provided by AAFMAA Wealth Management & Trust LLC is not intended to be tax or legal advice. Nothing contained in this communication should be interpreted as such. We encourage you to seek guidance from your tax or legal advisor. Past performance does not guarantee future results. Investments are not FDIC or SIPC insured, are not deposits, nor are they insured by, issued by, or guaranteed by obligations of any government agency or any bank, and they involve risk including possible loss of principal. No information provided herein is intended as personal investment advice or financial recommendation and should not be interpreted as such. The information provided reflects the general views of AAFMAA Wealth Management and Trust LLC but may not reflect client recommendations, investment strategies, or performance. Current and future financial environments may not reflect those illustrated here. Views of AAFMAA Wealth Management & Trust LLC may change based on new information or considerations.