Mega-cap tech pairwise correlation on QQQ worst-decile down days vs ordinary sessions (last ~3y)
Surprisingly, the six mega-cap names in QQQ were less synchronized on the worst-decile QQQ down days than on ordinary sessions: the mean pairwise Pearson r was about 0.239 on selloffs versus about 0.410 on other days. That gap runs counter to the common intuition that correlations spike toward 1 when the tape breaks.
I looked at close-to-close returns for AAPL, MSFT, NVDA, GOOGL, AMZN and META over roughly three years and compared the average of the 15 pairwise correlations on QQQ’s bottom-decile days to all other days. The full methodology, pairwise breakdowns, statistical test (paired t) and charts are below.
For QQQ over the past ~3 years, does Big-Tech diversification vanish in a selloff — is the average pairwise daily-return correlation among AAPL, MSFT, NVDA, GOOGL, AMZN, and META meaningfully higher on QQQ's worst-decile down days than on ordinary sessions? Thesis: pairwise correlations lurch toward 1 when the tape breaks, so the mega-caps move as one exactly when you'd need them to diverge, making intra-tech diversification a fair-weather illusion.
How this was measured
Constructed daily close-to-close returns for AAPL, MSFT, NVDA, GOOGL, AMZN, and META from minute bars, aligned over the past ~36 months. Identified QQQ bottom-decile days using the 10th percentile of QQQ daily returns (threshold -0.0144) as 'selloffs'; all remaining days are 'ordinary sessions'. Within each regime, computed the 6×6 Pearson correlation matrix of the mega-cap returns, extracted the 15 off-diagonal pairwise correlations, and summarized their average. A paired t-test across the 15 pairs (selloff vs ordinary) tests whether pairwise correlations systematically rise in selloffs.
The key numbers
The charts
Pairwise correlations by regime (15 pairs)
| pair | corr_selloff | corr_ordinary | delta |
|---|---|---|---|
| AAPL-MSFT | 0.2948 | 0.3194 | -0.0246 |
| AAPL-NVDA | -0.0577 | 0.3014 | -0.3591 |
| AAPL-GOOGL | 0.272 | 0.329 | -0.057 |
| AAPL-AMZN | 0.1082 | 0.3617 | -0.2535 |
| AAPL-META | 0.4349 | 0.2868 | 0.1481 |
| MSFT-NVDA | -0.009 | 0.4675 | -0.4765 |
| MSFT-GOOGL | 0.3816 | 0.3179 | 0.0637 |
| MSFT-AMZN | 0.3256 | 0.5102 | -0.1846 |
| MSFT-META | 0.3742 | 0.5212 | -0.1469 |
| NVDA-GOOGL | 0.1776 | 0.3537 | -0.1761 |
| NVDA-AMZN | 0.1122 | 0.4417 | -0.3294 |
| NVDA-META | 0.0536 | 0.4994 | -0.4458 |
| GOOGL-AMZN | 0.3454 | 0.4498 | -0.1044 |
| GOOGL-META | 0.4532 | 0.4008 | 0.0524 |
| AMZN-META | 0.3207 | 0.5823 | -0.2616 |
The takeaway
No — over the past ~3 years the mega‑caps did not converge toward r=1 on QQQ worst‑decile down days; average pairwise correlation was lower in selloffs, not higher. The mean pairwise Pearson r across the 15 stock pairs was about 0.239 on selloffs versus 0.410 on ordinary days, a difference of −0.170. That gap is statistically robust in this test: the paired t‑stat is −3.52 with a two‑sided p ≈ 0.0034 — only about a 3‑in‑1,000 chance this average shift is random under the paired comparison. The pattern isn’t uniform: only 20% of pairs were more correlated in selloffs, with large drops in some pairs (AAPL–NVDA fell roughly 0.30→−0.058, delta −0.359) while a few pairs rose (AAPL–META ~0.287→0.435). Practical takeaway: in this 740‑day sample (74 worst‑decile selloffs, 666 other days) intra‑tech diversification did not evaporate during the worst QQQ down days — correlations tended to fall on average — but that conclusion is specific to this period and these methods.
The fine print
- The paired t‑test treats the 15 pairwise correlations as independent, but pairs overlap and are themselves correlated.
- Pearson r on daily returns is sensitive to outliers; rank or robust measures could give a different picture.
- Selloffs are defined by an in‑sample bottom decile (QQQ ≤ −1.44%); different thresholds change which days are included.
- This result covers ~36 months (2023‑07‑19 to 2026‑06‑30); other market regimes may behave differently.