November 2025 Market Commentary

November 2025 Commentary

HOW CONCENTRATED IS S&P 500 RISK?

Over the past two years, we have written frequently about the attractiveness of US markets and what makes them exceptional. For example, supportive fiscal policy, reporting transparency and the strength of the regulatory environment have been key drivers of US corporate profitability and the outperformance of US large-cap equities over non-US companies since at least the Great Financial Crisis (GFC). In fact, the US has been both an incubator of technology and an engine of capital growth, a combination whose success has led to a paradoxical increase in US large-cap portfolio risk. To be more specific, the overall composition of the US market – represented by the S&P 500 – has become significantly more concentrated in fewer industry sectors and individual stocks than it was 10 or even five years ago. This change is important because investors who “own the market” have become increasingly exposed to the success or failure of a correspondingly small number of industries and companies.

If all sectors of the S&P 500 had benefitted equally over this time period, then risk concentration would not be the concern that it is today. However, we can see from Exhibit 1 that sector growth has been anything but equal with the growth in S&P 500 capitalization dominated by the Information Technology sector (we use SPY, an ETF that tracks the S&P 500, as a proxy for the index itself).

Exhibit 1. Historical Sector Weights Within SPY

Source: Bloomberg, AAFMAA Wealth Management & Trust

Indeed, over the period shown (11/2014 – 11/2025), the Information Technology sector has more than doubled as a percentage of S&P 500 capitalization and now comprises more than 35% of the index.

This trend has not been true internationally. As a comparison, Exhibit 2 shows the evolution of sector weights over a similar time period within the non-US developed equity markets using the ETF EFA as a proxy. The more stable and well-distributed sector weights outside the US are one of the reasons we decided to add exposure to developed world equities earlier this year.

Exhibit 2. Historical Sector Weights Within EFA

Source: Bloomberg, AAFMAA Wealth Management & Trust

A Closer Look at Risk Concentration at the Sector Level

While it’s concerning that a single sector comprises more than a third (35.24%) of S&P 500 capitalization, it’s even more so when we look at IT’s contribution to risk. As of November 12, 2025, the high volatility of the IT sector makes it responsible for almost half (47.8%) of the forecasted volatility of the S&P 500[1], suggesting that returns of the large-cap US equity market are becoming more and more leveraged to the success or failure of a relatively small number of companies.

We can gain insight into this risk by comparing the S&P 500, which is a market capitalization weighted index, with the equal-weighted S&P 500 index. To do this, we note that the contribution to risk of any individual stock in a portfolio depends on its weight in the portfolio, its return correlation with the overall portfolio, and its volatility (we can think of this as the inherent riskiness of the company measured by the expected dispersion of its future returns).

In the equal-weighted S&P 500, weightings are not a factor because the index is constructed to hold the same exposure to every stock. Therefore contributions to risk are defined solely by individual stock correlations with the index and their volatilities. Exhibit 3 shows a scatter plot of the implied volatility of every stock in RSP versus its correlation with the index broken out by industry sector. Although the plot has few distinctive features, we can still make a few observations.

• Stocks with higher correlations and higher volatilities contribute disproportionately to portfolio risk regardless of company size. Therefore, stocks to the right and toward the top contribute the most risk. Likewise, stocks to the left and toward the bottom reduce risk.

• Small stocks often contribute more risk to the portfolio than large stocks. For example, Delta Airlines (the large black point on the chart), a company with a market capitalization of about $38 billion, contributes twice the risk of Nvidia (the large red point on the chart), a company with a market cap of approximately $4.6 trillion. This disparity occurs even though their weightings in the index are the same. The two stocks’ volatilities are similar, but Delta’s correlation with the equal-weighted index is twice that of Nvidia.

• As a result, historically, the equal-weighted S&P 500 index has generally been riskier than the market capitalization-weighted S&P 500 index, though that is not true today.

Exhibit 3. Implied Volatility and Correlation with the Equal Weighted S&P 500 for Each Stock in RSP

Source: Bloomberg, AAFMAA Wealth Management & Trust

In contrast, Exhibit 4 shows a similar chart for every stock in SPY, but the points are sized to show the weight of each stock in the portfolio – reflecting their weights in the market capitalization-weighted S&P 500. In this plot, Delta Airlines and Nvidia are the two points just to the left of the black arrow. Because of their differences in size, Delta is barely visible on the chart, while Nvidia stands out as the largest point. We chose to highlight these two companies because their volatilities and correlations to the S&P 500 are very close to each other, but Nvidia is more than 100 times the size of Delta and therefore contributes more than 100 times the risk to SPY. It is clear that there are about a dozen other companies that are oversized, a number of which are from the Information Technology (Apple, Broadcom, Microsoft) sector, a couple from Consumer Discretionary (Amazon and Tesla), and several from other sectors. This visualization begins to explain why a single sector like IT can contribute close to half the risk of the entire S&P 500 index.

Exhibit 4. Implied Volatility and Correlation with the Cap Weighted S&P 500 for Each Stock in SPY

Source: Bloomberg, AAFMAA Wealth Management & Trust

Another way to look at this concept is through the use of a statistical technique called Principal Components Analysis (PCA). PCA is helpful because it calculates how many independent factors are needed to explain the behavior of a series of data over time. In this case, the data that we are interested in are the dollars of return contributed by every stock in the S&P 500 over roughly the past 10 years on a monthly basis. If every stock realized the same returns, then larger-weighted stocks would contribute more dollars of return than smaller-weighted stocks – and we might conclude that owning smaller-weighted stocks simply didn’t matter. Similarly if larger or smaller stocks systematically realized higher or lower returns, then PCA should also be able to help us see that in the data. Ultimately, we’d like to know how many independent factors explain contribution to return and how the number of factors have changed over time. Our hypothesis is that, as the S&P 500 has become more concentrated, fewer factors should be required to explain the return contributions. We can equivalently say that if the number of factors has decreased over time, then the S&P 500 has become less diversified in a meaningful way.

And as Exhibit 5 demonstrates, that is precisely what has happened. This chart shows the number of factors required to explain 95% of the variance in SPY return contributions over rolling 36-month periods from the beginning of 2015 through October 2025. A decade ago, the vast majority of the return contributions of the 500 stocks in the S&P 500 could be explained by 21 independent factors. Today that number is closer to 11.

Exhibit 5. Decline In S&P 500 Diversification Over Time

Source: Bloomberg, AAFMAA Wealth Management & Trust

So what exactly does that mean? Well, let’s start by asking what the factors are that this type of analysis identifies? Unfortunately, the statistical process can’t tell you what the factors are – only that they theoretically exist. However, with a little investigation, we can begin to identify some possibilities. It turns out that over the past 36 months, the factor that explains the highest percentage of variance in return contributions looks remarkably similar to the monthly percentage price changes of… Nvidia. And the factor that explains the second highest percentage of the variance looks a lot like the monthly percentage price changes of… Apple. Exhibit 6 shows these relationships. Each chart displays the monthly percentage price changes of Nvidia and Apple, respectively, compared with the first and second most important factor scores, respectively. The factor scores themselves are not meaningful for our purposes, but the correlation of the patterns they exhibit compared with the stock return histories are. What’s also fascinating is that the first two factors explain about 64% of the variance of S&P 500 individual stock return contributions over the past 36 months. Because the factors correlate closely with the returns of Nvidia and Apple, we can conclude that the other 498 companies in the index only account for about a third of the dollars of return.

Exhibit 6. Similarity of Monthly Price Changes of Nvidia and Apple to the Two Factors That Explain About 64% of the Variance in S&P 500 Individual Stock Return Contributions, Oct 2022 – Sep 2025

Source: Bloomberg, AAFMAA Wealth Management & Trust

This helps confirm that the dollars of return driving the S&P 500 over recent years has been driven by a limited universe of ultra large stocks. And, going back to Exhibit 5, we can also conclude that the diversification of the S&P 500 is at best half of what it was 10 years ago.

Final Thoughts

While we’ve only made moderate portfolio exposure shifts this year due to the rising risk concentration in the S&P 500, we are aware that extremes in sector or asset weights are often closely linked to macro trends. At the sector level, we’ve seen structural cycles increase relative Technology exposure during the emergence of computers in the 1960s, the internet bubble of the 1990s, and, most recently, the rise of the Magnificent 7. These sorts of trends have impact well beyond the relative sector value within US large-cap indices — they also affect the percentage of US and non-US developed market equities within a hypothetical Global Portfolio that contains all available assets on a capitalization-weighted basis.

In 2000, equity weight in the Global Portfolio trended down when the tech bubble burst. Equity weighting also trended down after the GFC. Since the GFC, the combination of quantitative easing and strong growth of US mega cap tech stocks has boosted equity weight back to the levels of the internet bubble. During this same period, the percentage of US bond exposure also increased relative to the rest of the world.

History is filled with examples of dominant asset classes retrenching with changes in macroeconomic trends or structural shifts. The high percentage of technology exposure in the S&P 500 and the high percentage of US equities within the Global Portfolio has potentially negative implications for relative expected returns necessary for the US to maintain those weightings. With the rise in risk concentration in US large cap equities and elevated US equity valuations, we continue to look for opportunities to diversify outside of US large cap.

Yours in trust,

Paul and Parker

[1] Using the Bloomberg PORT risk model.

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