Is SMCI a 'leveraged NVDA'? Beta vs idiosyncratic risk over the last ~3 years
SMCI does register a meaningful positive sensitivity to NVDA, but that sensitivity doesn’t tell the whole story: the single-factor fit produces a beta of about 1.07 while R² is only ≈0.277. In plain terms, SMCI moves with NVDA, yet roughly 72% of its daily return variance remains idiosyncratic — residual volatility is most of the picture, not a neat leveraged echo.
We tested overlapping daily returns over the last ~3 years with OLS (NVDA and a two-factor NVDA+SPY specification) and rolling betas/R² to capture time variation. The NVDA link is statistically robust, but the detailed tables and charts below show that treating SMCI as a simple leveraged NVDA trade would miss the majority of its risk profile. The evidence follows.
For SMCI over the past ~3 years, is it really just a leveraged bet on NVDA — do its daily returns track NVDA with a beta well above 1, or does company-specific risk dominate the moves? Thesis: SMCI carries a high beta to NVDA but a surprisingly low R-squared, so most of its volatility is idiosyncratic rather than an amplified NVDA echo, debunking the popular 'leveraged NVDA' label.
How this was measured
Resampled SMCI, NVDA, and SPY minute bars to daily closes and computed close-to-close returns. Ran OLS: (1) SMCI ~ alpha + beta_NVDA*NVDA; (2) SMCI ~ alpha + beta_NVDA*NVDA + beta_SPY*SPY. Interpreted beta and R-squared to gauge linkage vs idiosyncratic risk; residual/total vol ratio = sqrt(1−R²). Computed 60-day rolling beta (cov/var) and rolling R² (corr²) for time-variation. Window: last 36 months of overlapping daily data.
The key numbers
Reading the numbers
Across 751 trading days, SMCI has about a 1.07 beta to NVDA but NVDA only explains ~27.7% of its daily variance, so roughly 72.3% of SMCI's movement is idiosyncratic rather than just an amplified NVDA move.
The charts
The scatter plots each day's NVDA return (x) against SMCI return (y). Notice the cloud is fairly wide: NVDA runs from about -0.1536 to 0.2054 while SMCI spans roughly -0.3438 to 0.3888, so SMCI shows much larger swings on many days. The positive slope matches the reported beta (~1.07) and Pearson r (~0.526), but the broad vertical scatter and R^2 (~0.277) make clear NVDA only explains a minority of SMCI's daily moves.
This line shows the 60-day rolling beta of SMCI on NVDA; the series averages about 1.086 and begins at 1.1278 and ends near 0.9579, so most of the sample has beta at or above one, supporting the 'leveraged' intuition. But the beta itself is volatile: it ranges from a low of -0.2303 to a high of 1.8316, signalling episodes where the link weakens or flips and company-specific factors dominate.
The 60-day rolling R-squared shows how much of SMCI's variance is captured by NVDA over time; the mean is about 0.3581, it peaks at 0.6714, drops to 0 at the minimum, and is only 0.1432 at the last observation. Those frequent low-to-moderate R^2 readings mean NVDA often leaves most variance unexplained, which aligns with the headline idiosyncratic share (~72.3%) and the modest NVDA partial R^2 given SPY (~0.108): NVDA is important but far from the whole story.
OLS — SMCI on NVDA (single-factor)
| parameter | estimate | std_err | t_stat | p_value |
|---|---|---|---|---|
| const (alpha) | 2.0000e-06 | 0.001989 | 0.001 | 0.9994 |
| NVDA beta | 1.0693 | 0.0632 | 16.921 | 0 |
| R-squared | 0.2766 |
OLS — SMCI on NVDA and SPY (two-factor)
| parameter | estimate | std_err | t_stat | p_value |
|---|---|---|---|---|
| const (alpha) | -0.000349 | 0.001968 | -0.178 | 0.8591 |
| NVDA beta | 0.8154 | 0.0858 | 9.501 | 0 |
| SPY beta | 1.1944 | 0.2768 | 4.315 | 0 |
| R-squared | 0.2941 | |||
| NVDA partial R² (given SPY) | 0.1077 |
The takeaway
No — SMCI does show a meaningful positive sensitivity to NVDA, but it is not just a leveraged NVDA echo; most of SMCI's daily volatility is company-specific. The single-factor regression gives NVDA beta ≈ 1.07 while R² ≈ 0.277, which means about 72% of SMCI's return variance is idiosyncratic and residual volatility is roughly 85% of total. Adding SPY knocks the NVDA beta down to ≈ 0.82, SPY's beta is ≈ 1.19, and the two-factor R² only rises to ≈ 0.294 with NVDA's partial R² ≈ 0.108 — NVDA explains little unique variance beyond the market. The NVDA coefficient is statistically very strong (t ≈ 16.9, p ≈ 1.2e-54) over 751 trading days, so the beta is real, not a fluke, but its ability to explain day-to-day moves is modest. Bottom line: SMCI is NVDA-sensitive, but treating it as a simple leveraged NVDA trade would miss the majority of its risk, which is idiosyncratic.
The fine print
- Daily OLS assumes nice residuals; equity returns are heavy-tailed so t-stats and p-values can be optimistic.
- A few big events (earnings, guidance, offerings) can strongly affect beta and R²; outliers may be driving part of the result.
- 60-day rolling estimates are noisy with short windows; longer windows smooth but lag regime shifts.
- We used SPY as the only control; sector or AI-basket factors and intraday lead/lag effects could change attribution.