Every few months the market holds its breath for one company. Nvidia has turned its quarterly report into something closer to a market-wide event, the kind that can shift sentiment across entire sectors in a single session. I’ve watched this pattern long enough to notice that the real story often sits outside the chipmaker itself. Some names almost always move in lockstep the day after the numbers drop. Others quietly head the other way. Understanding that split can change how you position ahead of the next release.
Why Nvidia Earnings Now Move More Than Just One Stock
Wall Street is currently looking for adjusted earnings per share around $2.10 on roughly $92 billion in revenue for the latest quarter. Both figures sit nearly double the year-ago levels. Options markets are pricing a move of about five percent in either direction once the numbers hit after the bell. That kind of expected volatility does not stay contained. It spills into related names, into the broader technology complex, and sometimes into completely unrelated corners of the market.
I started tracking these post-earnings reactions several years ago, mainly out of curiosity. What began as a simple correlation check turned into a clearer map of how risk appetite travels. When Nvidia delivers a strong print and guidance that reinforces the AI infrastructure story, growth-oriented shares tend to catch a bid. When the report disappoints or the outlook feels cautious, money often rotates toward steadier businesses that look less dependent on the next data-center build-out.
The analysis behind these baskets looked at every earnings-reaction day going back to 2016. Three simple measures mattered most: the correlation of each stock’s daily move with Nvidia’s, the beta relative to Nvidia on those specific days, and the percentage of sessions in which the two stocks moved in the same direction. Minimum filters kept the results practical—at least thirty observations, market capitalization above two billion dollars, and thresholds that separated meaningful relationships from noise.
The Sympathy Group: Names That Tend to Travel With Nvidia
The list of stocks that historically move alongside Nvidia is dominated by semiconductors and companies tied directly to the physical build-out of AI capacity. That should surprise no one. Still, the ranking inside the group offers a few useful nuances.
Advanced Micro Devices sits at the top with a correlation of 0.75 and a beta of 0.28. On nearly seventy percent of the reaction days studied, AMD shares moved in the same direction as Nvidia. The relationship feels intuitive. Both companies compete and cooperate in the high-performance computing space, and investors often treat them as proxies for the same underlying demand story.
Vertiv ranks close behind. The company supplies power and cooling systems that data centers actually need to keep dense GPU clusters running. Its correlation comes in at 0.67 with a beta of 0.27. The trading history is shorter—only thirty-two observations—but the consistency is hard to ignore. When Nvidia’s numbers reinforce the case for more AI servers, demand expectations for the supporting infrastructure tend to rise as well.
Several deeper-cut semiconductor names also clear the screen. Onto Innovation posts a 0.64 correlation. Marvell Technology shows 0.59 and, interestingly, the highest same-direction rate among the larger names at seventy-one percent. Monolithic Power Systems follows at 0.57. Micron, Broadcom, Synopsys and Cadence Design Systems round out the group. Each of these businesses sits somewhere along the chip design, manufacturing or testing chain, so the read-through from Nvidia’s results feels natural.
I’ve found that the sympathy effect is strongest when Nvidia’s commentary focuses on sustained data-center growth rather than one-time inventory swings or competitive share shifts. In those sessions the entire ecosystem tends to reprice together. The beta numbers remain modest—most sit well below 0.3—because these stocks still carry their own idiosyncratic news flow. Yet the directional consistency is high enough to matter for short-term positioning.
The strongest relationships appear when the market treats Nvidia’s numbers as a real-time referendum on AI infrastructure spending rather than a narrow company-specific event.
The Anti-Nvidia Basket: Stocks That Often Head the Other Way
The inverse list looks completely different. Defensive, non-technology names dominate. These are businesses whose earnings streams do not depend on the next wave of GPU deployments. When risk appetite cools after a soft Nvidia print, capital often seeks the relative safety of predictable cash flows.
Pfizer shows a negative correlation of 0.61 and moved in the same direction as Nvidia only thirty-eight percent of the time. McDonald’s registers an even sharper divergence: negative 0.56 correlation and a same-direction rate of just seventeen percent. That second number is striking. On the large majority of Nvidia reaction days, McDonald’s shares have gone the opposite way.
Verizon, Philip Morris, Amgen and Johnson & Johnson also appear in the inverse group. Gold-related equities surface as well. Newmont, for example, carries a negative 0.54 correlation and same-direction frequency of only twenty-nine percent. The pattern fits a classic risk-on versus risk-off rotation. Strong Nvidia reactions tend to coincide with enthusiasm for growth and AI-linked stories. Softer reactions or profit-taking often lift defensive and hard-asset names.
It is worth stressing that these inverse relationships do not imply direct causation. Nvidia’s earnings do not cause Pfizer or McDonald’s to move. Instead, the two sets of stocks respond differently to the same shift in market psychology. When the growth trade is in favor, defensive names can lag or decline. When that trade loses momentum, the opposite occurs. The historical percentages simply quantify how consistently that rotation has played out on Nvidia reaction days.
How the Screen Was Built and Why the Filters Matter
Any historical study lives or dies by its filters. The analysis required a minimum of thirty observations so that a handful of outlier sessions could not dominate the results. Market capitalization had to exceed two billion dollars to keep the focus on names that most institutional investors can actually trade in size. Correlation thresholds of 0.45 (or negative 0.45) and a non-zero beta of the same sign helped separate real relationships from random noise.
I prefer these simple, transparent criteria over more elaborate statistical models. They are easy to update after each new earnings cycle and they do not hide the raw directional consistency behind complex coefficients. The same-direction percentage, in particular, has proven useful. A stock can show a moderate correlation yet still move the same way seventy percent of the time. That consistency is often more actionable for short-term trading than a pure correlation number.
One practical limitation is worth noting. Vertiv’s shorter history means its ranking rests on fewer data points than AMD or Broadcom. Newer names that have only recently become relevant to the AI story will take time to accumulate enough observations. The screen therefore favors companies that have been public and relevant for most of the 2016–2026 window.
What the Pattern Suggests About Market Psychology
Perhaps the most interesting aspect is how cleanly the two baskets map onto risk appetite. The sympathy group is almost pure growth and AI infrastructure exposure. The anti group is classic defensiveness plus a touch of gold as a store-of-value play. That split tells us something about how investors currently frame Nvidia’s results.
When the report is strong, the market treats it as confirmation that the multi-year AI capital-spending cycle remains intact. Capital flows toward the enablers of that cycle. When the report raises questions about near-term demand or margins, the same capital often rotates into businesses whose cash flows look less cyclical. The rotation is rarely dramatic on a single day, yet the historical consistency is high enough to notice.
In my experience the strongest rotations occur when Nvidia’s guidance language shifts. A single phrase about “sustained demand” or “customer inventory digestion” can change the tone of the entire session. The sympathy names usually react first and most forcefully. The defensive names tend to lag by a few hours or even into the following day as the broader risk tone settles.
Practical Ways to Use the Historical Relationships
None of this should be treated as a mechanical trading system. Correlations change, betas shift, and new competitive dynamics appear. Still, the historical map offers a few practical starting points.
- Ahead of the report, check relative positioning in the sympathy names versus the defensive group. Extreme overcrowding in one basket can amplify the post-earnings move.
- Watch the first hour of trading the next day. The initial reaction often sets the tone for the full session, especially for the higher-correlation names.
- Remember that options markets already price a roughly five percent move in Nvidia itself. The spillover into related stocks is usually smaller but more consistent in direction.
- Gold-related names and consumer staples can serve as quiet hedges if the growth narrative softens.
I tend to treat the sympathy list as a set of satellite ideas rather than core holdings. A strong Nvidia print can provide a short-term tailwind, yet each of those companies still carries its own product cycle and competitive risks. The inverse names work better as portfolio ballast than as pure anti-Nvidia bets. Their negative correlation is real on reaction days, but over longer periods many of them simply march to different fundamental drummers.
Limitations and Evolving Relationships
Markets evolve. The AI infrastructure build-out is still relatively young in market terms. New suppliers, new cooling technologies, and new custom silicon designs will appear. Some of today’s high-correlation names may see their relationships weaken as competition intensifies or as customers diversify their vendor base. Conversely, companies that currently sit outside the screen may develop stronger links if their products become more central to the data-center power and thermal story.
The defensive side of the ledger is also subject to change. Interest-rate cycles, inflation surprises, and shifts in consumer behavior can alter how capital treats traditional safe-haven names. A period of rising rates, for example, can pressure both growth stocks and certain defensive utilities at the same time, temporarily reducing the clean inverse relationship.
That is why the screen needs regular updating. Thirty observations is a useful minimum, yet the most recent ten or fifteen sessions often carry more weight for near-term positioning. I usually re-run the numbers after each major Nvidia report and after any significant shift in the broader market regime.
Putting the Numbers in Context for the Coming Report
With expectations sitting near double last year’s levels, the bar is high. A clean beat accompanied by confident commentary on data-center demand would likely support the sympathy group. A miss, or even a beat that comes with cautious language about near-term visibility, could favor the defensive and gold-related names. The options-implied five percent move already embeds a wide range of outcomes. The historical pattern simply helps map which other stocks have tended to respond in predictable ways.
One subtle point often gets overlooked. The absolute size of the move in Nvidia matters less than the tone of the guidance and the market’s interpretation of that tone. A modest beat with strong multi-year language can produce a larger spillover into the sympathy basket than a larger beat accompanied by hedging commentary. The correlation statistics capture average behavior; the qualitative details of each report still drive the extremes.
Broader Lessons for Earnings Season Positioning
Nvidia is an extreme case, yet the same logic applies to other high-visibility reports. When a single company becomes a proxy for an entire theme, its earnings day turns into a referendum on that theme. The stocks that benefit or suffer most are not always the ones with the tightest fundamental links. They are the ones that investors have chosen as liquid expressions of the same risk factor.
I’ve seen similar patterns around other platform companies in the past. The useful habit is to maintain a short list of both positively and negatively correlated names, update the statistics periodically, and treat the lists as context rather than trading signals. That approach keeps the focus on market psychology without pretending the relationships are permanent.
The current AI infrastructure cycle has simply made Nvidia the clearest real-time barometer. As long as that remains true, the sympathy and anti baskets will continue to offer a useful map of how capital is likely to rotate once the numbers are public.
A Closer Look at Individual Relationships
AMD’s high correlation is not accidental. The two companies share overlapping customer sets in the data-center and accelerated-computing markets. When Nvidia’s results reinforce the overall spending cycle, AMD often receives a secondary bid even if its own product cadence is on a different schedule. The 0.75 correlation and nearly seventy percent same-direction rate make it the clearest single proxy in the group.
Vertiv’s appearance near the top highlights a different dynamic. Power and cooling are no longer secondary considerations. Dense GPU racks generate heat and draw electricity at levels that older data-center designs struggle to handle. Investors have begun treating suppliers of those solutions as direct beneficiaries of the same capital-spending wave. The shorter observation window means the ranking could shift with more data, yet the early consistency is notable.
Marvell’s seventy-one percent same-direction rate is the highest among the larger names. Its mix of networking, storage and custom silicon exposure places it squarely in the path of AI infrastructure demand. The slightly lower correlation of 0.59 compared with AMD suggests more company-specific noise, yet the directional consistency remains impressive.
On the inverse side, McDonald’s seventeen percent same-direction rate stands out. Consumer staples and restaurant chains rarely compete for the same capital as high-growth technology names. When the market is in full risk-on mode after a strong Nvidia print, these stocks can lag simply because money is flowing elsewhere. When that mode reverses, the relative safety of steady cash flows becomes more attractive. The historical percentages simply quantify how often that rotation has coincided with Nvidia reaction days.
Gold miners such as Newmont add another layer. Gold often functions as a portfolio diversifier when growth narratives lose momentum. The negative 0.54 correlation and twenty-nine percent same-direction frequency fit that role. Again, the relationship is indirect. It reflects shifts in overall risk preference rather than any operational link to semiconductor demand.
How Investors Can Monitor the Relationships Over Time
The simplest ongoing check is to record the percentage move of Nvidia and each basket member on the trading day after every earnings release. After a few cycles a running correlation and same-direction count become clear. I keep a short spreadsheet that updates automatically once the daily closes are available. The process takes minutes and prevents the relationships from becoming stale assumptions.
Another useful habit is to note the qualitative tone of the conference call. Guidance language, commentary on customer inventories, and remarks about competitive intensity often explain why a particular reaction day produced a stronger or weaker spillover than the average historical relationship would suggest. Numbers alone never tell the full story.
Position sizing should stay modest. Even the highest-correlation names still experience independent news flow. A product delay, a competitor announcement, or a sector-wide valuation reset can overwhelm the Nvidia-related move on any given day. Treating the historical pattern as one input among several keeps expectations realistic.
The Role of Market Cap and Liquidity
The two-billion-dollar market-cap filter was deliberate. Smaller names can show high correlations simply because they are thinly traded and react more violently to any sector news. Including them would have produced a list full of high-beta micro-caps that most larger investors cannot use. By focusing on more liquid names the screen stays relevant for a broader set of market participants.
Liquidity also affects how quickly the relationships assert themselves. The larger sympathy names tend to open in the direction suggested by the overnight Nvidia move and then extend or reverse based on broader market tone. Smaller names can gap more dramatically and then mean-revert as the session progresses. The historical averages smooth over those intraday differences, yet they remain important for anyone trading the open.
Final Thoughts on Using the Map
Nvidia has become a real-time gauge of AI infrastructure enthusiasm. The stocks that historically move with it and those that historically move against it form a useful, if imperfect, map of how that enthusiasm travels through the market. The relationships are statistical rather than causal, and they can change as the technology and competitive landscape evolve. Still, the consistency over nearly a decade of earnings seasons is high enough to deserve attention.
Ahead of the next report the practical approach is straightforward. Know which names have tended to travel together, know which names have tended to diverge, and remember that the qualitative details of the report often matter more than the pure earnings surprise. The historical pattern supplies context. Judgment still supplies the final decision.
Markets will keep changing. New suppliers will enter the data-center ecosystem. Defensive sectors will face their own fundamental shifts. Updating the screen after each cycle keeps the map current. For now, the split between the sympathy group and the anti group remains one of the cleaner illustrations of how a single high-visibility earnings event can reshape risk appetite across the broader market.
That is the real takeaway. Nvidia’s numbers matter far beyond its own share price. They serve as a periodic stress test of the growth-versus-defensive rotation that has defined so many recent trading sessions. Watching both sides of that rotation—the names that move with the chip giant and the names that move against it—offers a clearer picture of where capital is likely to flow once the results are public.
