QQQM vs VYM: Correlation & Overlap
Invesco Nasdaq 100 ETF (QQQM) and Vanguard High Dividend Yield ETF (VYM) show a strong relationship: their 3-year correlation of weekly returns is 0.63. The two funds also share 14.9% of their portfolios by weight.
Data as of 2026-08-27 · refreshed every trading day · weekly returns · methodology
How correlated are QQQM and VYM?
On 3 years of weekly data the QQQM/VYM correlation comes out at 0.63, strong. The link has loosened recently: the 1-year correlation (0.47) runs below the 3-year figure (0.63). The 5-year figure is 0.66, and annualized covariance runs at 151.3 %².
Within QQQM's tracked universe of 103 assets, VYM comes in at #51 by 3-year correlation. Over the last 12 months QQQM came out ahead by 5.3 percentage points (+26.4% against +21.1%). On a rolling one-year basis the correlation drifted between 0.40 and 0.85, a moderate band. Note the risk asymmetry: QQQM runs 1.6 times the annualized volatility of the other leg, so equal-weighting the two is not an equal-risk position.
How is this computed?
Pearson correlation on weekly returns: ρ(A,B) = cov(rA, rB) / (σA · σB), over windows of 52, 156 and 260 weeks. Covariance is annualized (×52) and expressed in %². Full definitions on the methodology page.
QQQM vs VYM: side by side
| QQQM (Invesco Nasdaq 100 ETF) | VYM (Vanguard High Dividend Yield ETF) | |
|---|---|---|
| 1-year return | +26.4% | +21.1% |
| 5-year return | +96.1% | +76.6% |
| Volatility (ann.) | 19.5% | 12.3% |
| Beta vs S&P 500 | 1.28 | 0.69 |
| Max drawdown (3Y) | -22.7% | -14.5% |
| Dividend yield | 0.46% | 2.24% |
| Expense ratio | 0.15% | 0.04% |
| Assets under management | $97.1B | $99.2B |
| Sector / category | ETF · US Growth & Tech | ETF · Dividend |
On the fund side, QQQM sits in the Large Growth category at Invesco, with $97.1B under management, 105 holdings, a 0.15% expense ratio, a 0.46% trailing dividend yield. VYM, Vanguard's Large Value fund, carries $99.2B under management, 604 holdings, a 0.04% expense ratio, a 2.24% trailing dividend yield.
Portfolio overlap between QQQM and VYM
The two portfolios are largely distinct. Weighing the shared positions, 14.9% of the two funds is identical, spread across 28 common holdings. That shared book is a large part of why the returns line up.
| Common holding | Weight in QQQM | Weight in VYM |
|---|---|---|
| AVGO | 2.71% | 7.37% |
| CSCO | 1.96% | 1.87% |
| TXN | 1.06% | 1.02% |
| LIN | 1.00% | 0.90% |
| AMGN | 1.06% | 0.78% |
| PEP | 0.86% | 0.78% |
| ADI | 0.80% | 0.73% |
| GILD | 0.81% | 0.66% |
| QCOM | 0.76% | 0.63% |
| SBUX | 0.55% | 0.49% |
| ADP | 0.50% | 0.43% |
| CMCSA | 0.43% | 0.35% |
| TMUS | 0.86% | 0.34% |
| MDLZ | 0.36% | 0.33% |
| HON | 0.31% | 0.31% |
Largest positions held only by QQQM: NVDA (8.15%), AAPL (7.39%), MSFT (5.93%), MU (4.69%), AMZN (4.50%). Only by VYM: JPM (3.83%), XOM (2.63%), JNJ (2.51%), ABBV (1.81%), BAC (1.67%).
Overlap = sum of the smaller of the two weights across common holdings, from issuer disclosures as of 2026-08-26. Top 15 common positions shown.
Year-by-year returns
| Year | QQQM | VYM |
|---|---|---|
| 2022 | -32.5% | -0.4% |
| 2023 | +55.0% | +6.6% |
| 2024 | +25.7% | +17.6% |
| 2025 | +20.9% | +15.4% |
| 2026 | +17.7% | +15.9% |
Calendar-year price returns; the current year is year-to-date as of the data date above.
Are QQQM and VYM good diversifiers for each other?
Somewhat, no more. With 0.63 correlation, most large moves hit both names, and the diversification benefit stays modest.
FAQ
What is the correlation between QQQM and VYM?
As of 2026-08-27, the correlation of weekly returns between QQQM and VYM is 0.63 over 3 years, 0.47 over 1 year and 0.66 over 5 years.
Is VYM a good diversifier for QQQM?
Somewhat, no more. With 0.63 correlation, most large moves hit both names, and the diversification benefit stays modest.
How much do QQQM and VYM overlap?
The two funds share 28 holdings amounting to 14.9% of weight, per issuer portfolio files dated 2026-08-26.
Use this data
$ curl https://www.pairbook.io/api/v1/pairs/qqqm-vs-vym.json
Embed this badge (it refreshes with the data), with attribution:
[](https://www.pairbook.io/pair/qqqm-vs-vym/)
Free with attribution; caching and terms are described in the API documentation.
Related comparisons
Hubs: QQQM correlations · VYM correlations
