$NVDA $MU $SNDK $LITE EXECUTIVE SUMMARY
The 07/08/2025 to 07/08/2026 daily local-currency correlation matrix shows a common-factor-dominated TMT, semiconductor, AI infrastructure, and hardware universe with limited true diversification inside the highest-beta part of the group. Across the 435 unique pairwise relationships in the 30 x 30 matrix, the average correlation is approximately 0.474 and the median is 0.485. Excluding SPY and QQQ, the average pairwise correlation is still approximately 0.461 and the median is 0.467, indicating that the common factor is not solely an index artifact. Based on the rounded matrix inputs, the 1st principal component explains roughly 50.2% of total matrix variance including SPY and QQQ, and roughly 49.0% excluding SPY and QQQ. The implication is that gross exposure across this universe can look diversified by name count while remaining economically concentrated in the same AI, semiconductor-cycle, hardware-capex, rates-duration, and risk-appetite factor.
The most important portfolio construction conclusion is that correlation dispersion is highly asymmetric. There are many moderately positive correlations, very few truly low-correlation pairs, and no negative-correlation hedges inside the displayed universe. Only 12 of 435 total pairs have correlations of at least 0.700, and only 7 non-index pairs exceed 0.700. Only 3 non-index pairs are at or below 0.200: MXL/NBIS at 0.153, INTC/NBIS at 0.167, and ACMR/DOCN at 0.179. This creates 2 different types of advantage. High-correlation pairs are advantageous for relative-value, pair-trade, and idiosyncratic-alpha isolation. Low-correlation names are advantageous for portfolio diversification and idiosyncratic alpha sleeves, but they are generally poor hedge instruments for high-beta semiconductor or AI infrastructure longs.
MARKET FACTOR AND INDEX IMPLICATIONS
SPY and QQQ correlate at 0.930, confirming that the market and technology factors were highly compressed during the period. QQQ is the more relevant hedge proxy for the semiconductor and AI hardware complex. QQQ correlations are 0.740 with EWY, 0.740 with LRCX, 0.737 with TSM, 0.708 with KLAC, 0.690 with NVDA, 0.683 with AMAT, and 0.672 with MU. SPY correlations are materially lower for most of those same names, including 0.646 with LRCX, 0.585 with KLAC, 0.654 with NVDA, 0.569 with AMAT, and 0.526 with MU. The implication is that SPY hedges broad risk-off exposure, but QQQ is a more efficient hedge for technology beta and semi-capex beta. However, QQQ alone is not sufficient to neutralize subcluster risk, particularly WFE, memory, and Asia semiconductor exposure.
LRCX is the most central single name in the matrix. Excluding SPY and QQQ, LRCX has an average correlation of 0.593 with the rest of the universe, followed by AMAT at 0.557, KLAC at 0.550, TSM at 0.528, JBL at 0.519, MU at 0.515, and EWY at 0.513. These names are the highest common-factor transmitters. Long exposure to several of these names should be treated as concentrated exposure to a shared semiconductor-capex factor rather than as diversified stock selection. By contrast, DOCN has an average ex-index correlation of only 0.274, NBIS is 0.333, MXL is 0.336, CRDO is 0.393, INTC is 0.398, and NVDA is 0.407. DOCN, NBIS, and MXL therefore provide the cleanest correlation diversification in the displayed universe, though low correlation should not be confused with low risk.
WFE CLUSTER: STRONGEST RELATIVE-VALUE COMPLEX
The AMAT/KLAC/LRCX cluster is the cleanest and most actionable pair-trading complex in the matrix. AMAT/LRCX is 0.887, KLAC/LRCX is 0.878, and AMAT/KLAC is 0.870. These are the only 3 non-index relationships above 0.800. The average internal correlation of the 3-name WFE basket is 0.878, which means the basket behaves much closer to 1 effective equal-volatility bet than to 3 independent positions. Under a simple equal-volatility assumption, a 3-name equal-weight basket with 0.878 average internal correlation has an effective independent position count of only about 1.1. This is a critical risk point: owning AMAT, KLAC, and LRCX together creates very little diversification unless the alpha thesis is explicitly relative within WFE.
These same correlations make WFE the best subcluster for relative-value implementation. In standardized return terms, an equal-risk AMAT/LRCX spread would have approximate spread volatility of 0.48x a single-stock volatility, KLAC/LRCX would be approximately 0.49x, and AMAT/KLAC would be approximately 0.51x, before actual volatility, borrow, liquidity, and event-risk adjustments. That is materially tighter than the spread math for most other candidate pairs in the matrix. The conclusion is that WFE is the highest-quality pair-trade sandbox, especially for views on order share, China exposure, memory versus logic mix, installed-base service quality, margin durability, backlog conversion, and relative valuation. It is not an attractive diversification basket.
The WFE correlations are also robust after controlling for broad market or QQQ exposure. Using the rounded matrix inputs, AMAT/LRCX remains approximately 0.769 on a partial-correlation basis after controlling for SPY and QQQ, KLAC/LRCX remains approximately 0.738, and AMAT/KLAC remains approximately 0.731. This indicates that WFE co-movement is structural and not merely a function of common index beta. That strengthens the case for WFE pair trades, but it also raises crowding risk: in a factor unwind, the 3 names are likely to move together with high intensity.
MEMORY, STORAGE, AND KOREA LINKAGE
The memory/storage complex is the 2nd most actionable correlation cluster, though it is less tight than WFE. MU/LRCX is 0.747, MU/SNDK is 0.733, MU/EWY is 0.721, MU/AMAT is 0.690, SNDK/STX is 0.652, SNDK/LRCX is 0.646, and MU/STX is 0.609. The MU/LRCX correlation is particularly important because it links memory product-cycle exposure with semi-equipment capex exposure. That relationship can be useful for hedging memory-cycle beta, but it is not a clean product-for-product hedge. LRCX carries WFE order-cycle, China restriction, customer capex, and equipment-specific risk that can diverge sharply from MU’s pricing, inventory, and HBM mix.
MU/SNDK is the cleanest high-correlation pair within the memory/storage grouping. A 0.733 correlation implies meaningfully lower spread noise than most other non-WFE pairs, although the standardized spread volatility is still approximately 0.73x a single-stock volatility, materially higher than WFE pair spreads. SNDK/STX at 0.652 and MU/STX at 0.609 suggest storage-cycle adjacency, but these are less precise hedges and should be treated as thematic rather than pure statistical substitutes. A memory/storage relative-value basket built around MU, SNDK, and STX has an average internal correlation of roughly 0.665 and an equal-volatility effective independent position count of only about 1.3, indicating that the basket still carries substantial common-cycle exposure.
EWY is highly relevant to the memory and semiconductor cycle. EWY correlates 0.740 with QQQ, 0.721 with MU, 0.694 with LRCX, 0.649 with ASX, 0.645 with TSM, 0.628 with AMAT, and 0.613 with KLAC. In local-currency terms, EWY is behaving as a broad Korea technology and memory-cycle proxy rather than a clean country diversifier. This makes EWY a useful basket hedge for broad Asia semi and memory exposure, but it embeds macro, country, index composition, and FX translation considerations that are not captured by local-currency equity correlations.
ASIA SEMICONDUCTOR BASKET
The TSM/EWY/ASX grouping is a coherent Asia semiconductor basket. TSM/EWY is 0.645, TSM/ASX is 0.647, and EWY/ASX is 0.649, creating an unusually balanced 3-way relationship. The average internal correlation of this basket is 0.647, and the equal-volatility effective independent position count is approximately 1.3. The basket is therefore not highly diversified internally, but it can be highly useful as a broad Asia semi factor proxy.
This Asia semi basket has strong relationships with the core capex complex. In equal-risk basket terms, TSM/EWY/ASX correlates approximately 0.801 with QQQ, 0.699 with SPY, 0.776 with LRCX, and 0.713 with MU. This means the basket is potentially advantageous for hedging broad semiconductor beta and Asia hardware beta, particularly when the book contains TSM, WFE, MU, or Korea-linked exposure. However, because the matrix is in local currency, actual USD portfolio correlations can differ materially if FX is unhedged. KRW, TWD, and broader Asia FX exposures can add or subtract from realized hedge effectiveness in a USD-denominated P&L book.
NVDA: LESS CORRELATED THAN THE NARRATIVE WOULD SUGGEST
NVDA is not the central correlation node in this matrix despite its importance to the AI complex. NVDA correlates 0.690 with QQQ, 0.654 with SPY, 0.645 with TSM, 0.517 with VRT, 0.489 with KLAC, 0.486 with LRCX, 0.479 with JBL, 0.476 with EWY, 0.472 with FPS, and 0.455 with CRDO. NVDA’s average ex-index correlation is only 0.407, below most of the WFE, foundry, EMS, and hardware names. NVDA also has notably low correlations with DOCN at 0.210, MXL at 0.256, INTC at 0.307, STX at 0.322, SNDK at 0.323, AEHR at 0.341, CIEN at 0.342, LITE at 0.352, and VSH at 0.356.
The key implication is that NVDA cannot be cleanly hedged by simply shorting the broader semi-capex basket. TSM is the best single-stock hedge candidate in the matrix at 0.645, while QQQ is the best liquid index proxy at 0.690. VRT at 0.517 and CRDO at 0.455 are AI-adjacent but not high-correlation substitutes. WFE names also leave substantial NVDA-specific residual exposure: NVDA/LRCX is 0.486, NVDA/KLAC is 0.489, and NVDA/AMAT is only 0.427. After controlling for SPY and QQQ using the rounded matrix inputs, NVDA/TSM falls to roughly 0.281 and NVDA/VRT falls to roughly 0.203, indicating that a meaningful portion of their raw co-movement is broad factor exposure rather than highly specific shared alpha.
For hedge construction, the matrix favors QQQ plus TSM as the core NVDA hedge framework rather than WFE alone. Under a standardized-return regression using only correlations, QQQ alone explains roughly 47.6% of NVDA’s daily variance. QQQ plus TSM raises the implied explanatory power to roughly 51.7%, corresponding to an optimized basket correlation of approximately 0.719. Adding VRT and CRDO provides only incremental improvement, lifting the optimized basket correlation to roughly 0.725. This suggests that NVDA’s residual is still large even after adding AI-adjacent infrastructure and connectivity names. Event risk, earnings risk, customer concentration, margin-cycle risk, and product-transition risk remain difficult to hedge with this universe alone.
AI INFRASTRUCTURE, POWER, EMS, AND HARDWARE SUPPLY CHAIN
VRT is moderately connected to both semiconductor and infrastructure factors, but it is not a pure NVDA hedge. VRT correlates 0.615 with TSM, 0.613 with GEV, 0.609 with LRCX, 0.585 with AMAT, 0.579 with QQQ, 0.569 with JBL, 0.569 with GLW, 0.561 with FPS, and 0.559 with KLAC. The VRT/NVDA correlation of 0.517 is meaningful but not tight. The VRT/GEV pair at 0.613 is the most intuitive infrastructure relationship in the matrix and may be useful for relative-value work around electrification, grid equipment, power availability, data-center buildout, and industrial AI capex. However, 0.613 is not high enough to treat the pair as a low-noise statistical substitute.
The AI power and hardware infrastructure basket consisting of VRT, GEV, JBL, and GLW has an average internal correlation of roughly 0.558 and an equal-volatility effective independent count of approximately 1.5. This basket is more diversified than WFE or memory, but it still has substantial common factor exposure. Its correlations appear stronger with the broad semiconductor supply chain than with NVDA itself. In equal-risk basket terms, the VRT/GEV/JBL/GLW basket correlates approximately 0.742 with LRCX, 0.721 with TSM, 0.701 with QQQ, and 0.555 with NVDA. The implication is that this basket is better characterized as AI-capex and hardware-infrastructure beta than as a direct NVDA hedge.
JBL and TTMI form another notable hardware supply-chain relationship. JBL/TTMI is 0.661, one of the stronger non-index, non-WFE pairs in the matrix. JBL also correlates 0.651 with LRCX, 0.620 with GLW, 0.614 with KLAC, 0.610 with AMAT, 0.603 with FPS, and 0.599 with CIEN. This positions JBL as a broad hardware manufacturing and supply-chain beta node. TTMI is less index-sensitive than JBL but still has meaningful correlations with CIEN at 0.586, LRCX at 0.581, GLW at 0.560, LITE at 0.546, and SMTC at 0.545. JBL/TTMI appears to be an attractive pair candidate when the investment thesis is specific to EMS, PCB, server supply chain, product mix, or margin normalization.
OPTICAL, NETWORKING, AND CONNECTIVITY
The optical/networking cluster is real but less compressed than WFE. LITE/CIEN is 0.653, CIEN/GLW is 0.620, LITE/GLW is 0.598, CIEN/JBL is 0.599, CIEN/TTMI is 0.586, and SMTC/GLW is 0.576. The LITE/CIEN pair is the cleanest relationship in this group. After controlling for SPY and QQQ using the rounded matrix, LITE/CIEN remains roughly 0.558, which is strong for a non-WFE pair. This indicates that the relationship is not merely QQQ beta. The pair is therefore potentially useful for relative-value expressions around optical demand, telecom/datacom capex, margin recovery, inventory digestion, and AI-related optical intensity.
CRDO is more idiosyncratic than the AI networking narrative might imply. CRDO correlates 0.548 with QQQ, 0.539 with ASX, 0.478 with TSM, 0.467 with LRCX, 0.467 with VRT, 0.459 with JBL, 0.455 with NVDA, 0.451 with SMTC, and 0.450 with KLAC. These are moderate relationships, not tight hedges. CRDO/DOCN is only 0.206, CRDO/VSH is 0.292, CRDO/STX is 0.310, and CRDO/MXL is 0.319. For pair construction, CRDO should be treated as a high-idiosyncratic-growth name with moderate QQQ and Asia semi beta rather than as a clean substitute for NVDA, VRT, or the optical group.
LOW-CORRELATION DIVERSIFIERS AND IDIOSYNCRATIC SLEEVES
DOCN is the clearest diversification asset in the matrix. DOCN correlates only 0.210 with NVDA, 0.219 with TSM, 0.236 with VRT, 0.213 with GEV, 0.206 with CRDO, 0.179 with ACMR, 0.213 with MXL, 0.215 with TTMI, 0.224 with GLW, and 0.241 with ASX. Even its correlations with SPY and QQQ are modest at 0.348 and 0.378. The implication is that DOCN can reduce common semiconductor and AI hardware factor exposure in a broader TMT book. It should not be used as a hedge for semiconductor longs, because low correlation means the spread will be noisy and fundamentally unrelated. Its value is diversification and idiosyncratic alpha, not beta offset.
NBIS and MXL are also low-correlation assets. NBIS has correlations of 0.153 with MXL, 0.167 with INTC, 0.220 with ASX, 0.275 with DOCN, 0.297 with SNDK, 0.299 with TTMI, 0.301 with LITE, 0.309 with GLW, and 0.321 with GEV. MXL has correlations of 0.153 with NBIS, 0.211 with FPS, 0.213 with DOCN, 0.226 with GEV, 0.256 with NVDA, 0.266 with CIEN, 0.289 with STX, and 0.307 with VRT. The combination of DOCN, NBIS, and MXL has an average internal correlation of only approximately 0.214 and an equal-volatility effective independent count of about 2.1. That is materially more diversifying than any of the WFE, memory, Asia semi, or hardware infrastructure baskets.
INTC is also less correlated with the AI semi complex than a simple sector label would suggest. INTC correlates only 0.307 with NVDA, 0.374 with TSM, 0.486 with AMAT, 0.515 with KLAC, 0.502 with LRCX, and 0.535 with QQQ. Its highest notable relationship is MXL at 0.522, suggesting a legacy, PC, communications, or mixed-signal adjacency rather than clean participation in the AI accelerator and WFE cluster. INTC is therefore not a high-quality hedge for NVDA or the broader AI semi basket. It may be more useful as an idiosyncratic relative-value leg where the alpha thesis is company-specific, but the correlation matrix does not support treating INTC as a clean semiconductor beta substitute.
ADVANTAGEOUS PAIRS BASED ON CORRELATION
The most advantageous high-correlation pair complex is AMAT/KLAC/LRCX. Any 2-way pair inside this 3-name group is statistically attractive for relative-value implementation. AMAT/LRCX at 0.887 is the highest-quality pair in the matrix, followed closely by KLAC/LRCX at 0.878 and AMAT/KLAC at 0.870. These pairs are most appropriate for alpha views where the fundamental thesis differentiates order exposure, customer mix, China exposure, margin structure, valuation, or product-cycle durability while the portfolio objective is to minimize broad WFE factor noise.
The 2nd tier of advantageous pair candidates includes MU/SNDK at 0.733, SNDK/STX at 0.652, and MU/STX at 0.609 for memory/storage; LITE/CIEN at 0.653 and CIEN/GLW at 0.620 for optical/networking; JBL/TTMI at 0.661 for hardware manufacturing and PCB exposure; TSM/ASX at 0.647 and EWY/ASX at 0.649 for Asia semiconductor beta; and GEV/VRT at 0.613 for power infrastructure and data-center electrification. These pairs are actionable but should be sized with more conservative spread-risk assumptions than WFE because correlations in the 0.600 to 0.660 range still leave substantial idiosyncratic basis risk.
Pairs that look superficially thematic but are not statistically tight include NVDA/VRT at 0.517, NVDA/CRDO at 0.455, NVDA/LRCX at 0.486, NVDA/AMAT at 0.427, CRDO/VRT at 0.467, CRDO/SMTC at 0.451, and GEV/NVDA at 0.432. These relationships may still be investable on fundamentals, but the matrix does not support them as clean pair trades. They are more appropriate as thematic baskets with explicit risk limits than as low-noise long/short hedges.
ADVANTAGEOUS BASKETS BASED ON CORRELATION
The most useful hedge basket for WFE exposure is a concentrated AMAT/KLAC/LRCX basket, but this basket should be viewed as a factor hedge, not diversification. Its average internal correlation of 0.878 makes it the best cluster for shorting against a favored WFE long or for hedging semi-equipment factor exposure embedded in a larger book. A 6-name core semi beta basket consisting of TSM, AMAT, KLAC, LRCX, MU, and EWY has an internal average correlation of roughly 0.708 and a QQQ correlation of roughly 0.820 under equal-risk assumptions. This is an efficient broad semi-cycle hedge, but it behaves like approximately 1.3 effective independent equal-volatility positions, so risk should be treated as concentrated.
The most useful memory/storage basket is MU/SNDK/STX, with EWY or LRCX added only if the objective is to hedge broader memory-cycle or memory-capex beta. MU/SNDK is the cleanest product-adjacent pair, while MU/LRCX and MU/EWY are stronger broad-cycle relationships. A memory long hedged with SNDK/STX is more product-cycle aligned; a memory long hedged with LRCX/EWY is more macro-cycle and capex-factor aligned. The correct construction depends on whether the intended residual exposure is pricing, capex, HBM, Korea risk, NAND/HDD storage risk, or semi-factor beta.
The most useful Asia semi basket is TSM/EWY/ASX. This basket has a balanced internal correlation profile near 0.647 across all 3 pairings and aligns well with QQQ, LRCX, and MU. It is advantageous when the desired exposure is broad Asia semiconductor beta rather than a single-stock view. The main limitation is that local-currency correlations do not capture the full USD portfolio risk of KRW, TWD, and broader Asia FX.
The most useful AI infrastructure basket is VRT/GEV/JBL/GLW. This basket has moderate internal correlation of roughly 0.558 and captures power, electrification, hardware manufacturing, and physical data-center supply-chain beta. It is more diversified than WFE or memory, but it is not a clean NVDA hedge. Its stronger relationship with TSM and LRCX than with NVDA suggests that the basket is better used to express or hedge AI capex infrastructure breadth rather than GPU-specific risk.
The most useful diversification basket is DOCN/NBIS/MXL, with INTC potentially included depending on the fundamental mandate. This basket has low internal correlation and weak ties to the WFE and AI infrastructure complex. It is advantageous for reducing common-factor concentration in a TMT portfolio, but it is not advantageous for pair-trading against high-beta semiconductor longs. Low-correlation baskets lower portfolio common-factor exposure; they do not provide precise downside hedges during semiconductor-specific drawdowns.
RISK MANAGEMENT IMPLICATIONS
The matrix argues for explicit factor budgeting. A portfolio long TSM, LRCX, AMAT, KLAC, MU, EWY, JBL, and VRT would have high apparent name diversification but substantial latent exposure to a common AI hardware and semiconductor-capex factor. QQQ hedges part of that risk, but WFE-specific and Asia semi-specific baskets are needed if the objective is to reduce subcluster exposure. Conversely, a portfolio with DOCN, NBIS, MXL, INTC, and selected lower-correlation software or communications names would have more idiosyncratic dispersion but weaker capacity to hedge broader semi drawdowns.
Correlation levels should also inform spread-risk expectations. The WFE pair complex is meaningfully more statistically robust than the rest of the matrix. A 0.887 correlation pair has much lower expected spread volatility than a 0.650 correlation pair, and materially lower than a 0.500 correlation thematic pair. The practical implication is that gross sizing can be higher for WFE relative-value trades than for thematic AI infrastructure or networking pairs, all else equal. However, high correlation also increases crowding risk and creates the possibility of violent spread moves around company-specific earnings, export restrictions, customer capex revisions, or order-share surprises.
The matrix contains no true internal defensive hedge. All correlations are positive, and stress correlations are likely to rise in market drawdowns. Portfolio protection therefore requires instruments outside the displayed universe, such as index futures, sector ETFs, options, volatility overlays, rates-duration hedges, FX hedges, or explicit de-grossing triggers. Inside the matrix, the best that can be achieved is relative hedging, not true negative-correlation protection.
METHODOLOGICAL CAVEATS
The matrix is daily, trailing 1-year, and local currency. Daily correlations capture high-frequency co-movement, not full-cycle fundamental relationships. A 1-year window can be heavily shaped by a specific regime, in this case likely AI capex intensity, semiconductor-cycle expectations, rates moves, export-control headlines, and risk-on/risk-off factor compression. Correlations around 0.300 to 0.550 should not be over-interpreted at fine levels of precision; differences of 0.03 to 0.05 are often not economically robust without additional return, volatility, and drawdown analysis. The strongest conclusions are the WFE triad, the MU/SNDK and MU/LRCX relationships, the TSM/EWY/ASX Asia semi cluster, the LITE/CIEN optical relationship, and the low-correlation status of DOCN, NBIS, and MXL.
Correlation is not hedge ratio. Proper hedge construction requires volatility, beta, liquidity, borrow, event calendar, options-implied move, factor exposure, and tail-correlation analysis. A high-correlation pair can still lose money if relative fundamentals diverge, and a low-correlation pair can still suffer simultaneous drawdowns in stress. The matrix is therefore best used as a map of factor overlap, diversification quality, and relative-value viability rather than as a standalone signal for trade direction.
BOTTOM LINE
The best correlation-supported relative-value opportunity set is AMAT/KLAC/LRCX. The best 2nd-tier pair clusters are MU/SNDK, SNDK/STX, LITE/CIEN, JBL/TTMI, TSM/ASX/EWY, and VRT/GEV. The best broad semi beta hedge basket is TSM/AMAT/KLAC/LRCX/MU/EWY, with QQQ as the liquid overlay. The best NVDA hedge framework is QQQ plus TSM, with VRT and CRDO adding only limited incremental correlation benefit. The best diversification sleeve is DOCN/NBIS/MXL, potentially with INTC, but those names should be viewed as idiosyncratic risk absorbers rather than hedges for semiconductor longs. The key portfolio implication is that the universe contains high factor concentration in WFE, memory, Asia semi, and AI hardware infrastructure, while the true diversifiers are few and mostly outside the core AI semiconductor beta complex.
