Memory chip stocks screener excel is the tool you want open after a half-year like this one. Through the first half of 2026 the memory complex went from the most hated corner of semiconductors to the single best-performing theme in the market. Micron is up roughly 248 percent year to date, SanDisk has run about 736 percent since its spin-off, and the equipment and intellectual-property names that sit upstream of high-bandwidth memory have quietly compounded alongside them. The driver is simple to name and hard to forecast: artificial-intelligence accelerators consume enormous amounts of high-bandwidth memory, or HBM, and the industry cannot build it fast enough. This guide shows you how to build a working memory chip stocks screener in Excel with live MarketXLS formulas, and it ships with a downloadable dashboard that scores the whole group for you.
The post pairs with a downloadable covering twelve memory and memory-levered names, a composite Memory Score blending momentum, valuation and quality, an HBM demand scenario model, a technicals read, and a position-sizing sheet. Everything below is educational analysis of how to organize the data, not a recommendation to buy or sell anything.
Key Data Table - The Memory Chip Universe
The table below groups the universe into the buckets that actually behave differently in a memory cycle. Pure-play DRAM and NAND makers carry the most direct price-and-volume leverage. Storage names blend drives with chips. The equipment, materials and IP names sell into every memory fab regardless of which vendor wins. The numbers are illustrative snapshots for context as of June 29, 2026, not quotes, and every value updates live inside the template.
| Ticker | Company | Segment | Illustrative YTD | Why It Matters |
|---|---|---|---|---|
| MU | Micron Technology | DRAM / HBM | ~248% | The U.S. HBM pure-play, sold out well into 2027 |
| SNDK | SanDisk | NAND / Storage | ~736% | NAND spin-off riding enterprise SSD demand |
| WDC | Western Digital | HDD / Storage | ~142% | Nearline drives for AI data lakes |
| STX | Seagate Technology | HDD / Storage | ~118% | HAMR drive ramp, tight HDD supply |
| RMBS | Rambus | Memory IP / Buffer | ~64% | Memory interface chips on every DDR5 module |
| LRCX | Lam Research | Memory Equipment | ~86% | Etch and deposition, levered to memory capex |
| AMAT | Applied Materials | Memory Equipment | ~52% | Broad equipment exposure to DRAM and NAND |
| KLAC | KLA Corporation | Process Control | ~78% | Inspection tools as nodes shrink |
| ENTG | Entegris | Materials | ~41% | Specialty materials and filtration |
| ONTO | Onto Innovation | Metrology | ~69% | HBM packaging inspection |
| MRVL | Marvell Technology | Custom Si / HBM | ~47% | Custom accelerators and HBM connectivity |
| NVDA | NVIDIA | HBM Demand Driver | ~58% | The buyer whose appetite sets the cycle |
The point of laying it out this way is that "memory chip stocks" is not one trade. It is at least three: the cyclical price-takers, the picks-and-shovels suppliers, and the demand driver. A good screener lets you see all three at once and compare them on the same yardsticks.
What Is Driving the AI Memory Cycle in 2026
Every modern AI accelerator pairs its logic die with stacks of high-bandwidth memory. As model sizes and context windows grew through 2025 and into 2026, the bottleneck for training and inference shifted from raw compute toward memory bandwidth and capacity. HBM is built by stacking DRAM dies vertically and connecting them with through-silicon vias, a process that consumes far more wafer capacity per usable bit than commodity DRAM. When a memory maker shifts a wafer to HBM, it pulls supply out of the conventional DRAM market, which tightens pricing there too.
That is the mechanism behind the 2026 run. Demand for HBM is being set by hyperscaler capital-expenditure budgets that have kept climbing, while supply is gated by how fast Micron and its two Asian competitors can add advanced packaging capacity. Tight supply plus inelastic demand equals pricing power, and pricing power in a high-fixed-cost industry like memory flows almost entirely to the bottom line. That is why operating margins and earnings estimates for the group have been revised up so aggressively this year.
The cyclical caveat matters just as much. Memory has always been a boom-and-bust industry. The same operating leverage that produces 300 percent earnings growth on the way up produces brutal contractions on the way down when supply catches demand. A screener does not predict the turn, but it does let you watch the signals that tend to precede one: decelerating revenue growth, falling margins, rising inventory, and price extension far above moving averages. Tracking those in one place is the entire purpose of the template.
How to Build a Memory Chip Stocks Screener in Excel
The core of any screener is a single row per ticker with the metrics you care about pulled live. With MarketXLS installed, you reference a ticker once and let the functions fetch everything else. Here is the spine of the main dashboard, verified against the MarketXLS function library.
Price =QM_Last("MU")
Market cap =MarketCapitalization("MU")
Trailing P/E =PERatio("MU")
Forward P/E =ForwardPE("MU")
Price to sales =PriceToSales("MU")
Gross margin =GrossMargin("MU")
Operating margin =OperatingMargin("MU")
Revenue growth =RevenueGrowth("MU")
Return on equity =ReturnOnEquity("MU")
Beta =Beta("MU")
52-week change =FiftyTwoWeekChange("MU")
% below 52w high =PercentBelowFiftyTwoWeekHigh("MU")
RSI =RSI("MU")
Drop those across columns, list your tickers down the rows, and you have a live screener. Margins and growth come back as decimals, so multiply by 100 if you want to display percentages. Market cap comes back in full dollars, so divide by one billion for a clean "$B" column. None of these functions are invented for this post; each one exists in the MarketXLS library and is used in the attached workbook.
For deeper fundamental work you can extend the same pattern with functions like =Revenue("MU"), =GrossProfit("MU"), =QuarterlyRevenueGrowthYoY("MU"), and =EarningsPerShare("MU"). The discipline that keeps a screener trustworthy is simple: every number should be a formula, never a value you typed in and forgot to update. Learn more about the full library on the MarketXLS stock functions reference.
The Composite Memory Score
Raw metrics are useful but hard to rank at a glance, so the template computes a Memory Score from 0 to 100 for each name. The score blends three ideas that matter in a memory cycle:
- Momentum (about 40 points): RSI and 52-week trend strength. Memory tends to trend hard in both directions, so persistent strength is informative, while extreme readings flag extension.
- Quality (about 35 points): operating margin and return on equity. In a high-fixed-cost industry, margin is the cleanest read on where a company sits in the cycle.
- Valuation (about 25 points): forward P/E, where a lower multiple scores higher. This is the counterweight that keeps the most extended names honest.
The weighting is a starting point, not gospel. The whole formula lives in plain cells on the screener sheet so you can re-weight it to match your own framework. Maybe you care more about valuation than momentum, or you want to add a balance-sheet factor like net debt to equity. The template is built to be edited, not just admired. A score is a sorting aid, not a signal, and nothing in the workbook should be read as a recommendation.
HBM Demand Scenario Analysis
Because memory is cyclical, a point estimate of fair value is less useful than a range of outcomes. The Scenario Analysis sheet lets you pick one ticker, enter your share count and entry price in the yellow input cells, and see how the position behaves across five HBM-demand scenarios.
| Scenario | HBM Demand Assumption | Illustrative Price Move |
|---|---|---|
| Bull | HBM tight into 2027, pricing power holds, capex accelerates | +35% |
| Base+ | Demand steady, modest price gains, supply disciplined | +15% |
| Base | Balanced market, flat-to-mild pricing | 0% |
| Base- | Inventory builds, some price give-back | -12% |
| Bear | AI capex digestion, oversupply, memory down-cycle | -30% |
The price moves are illustrative assumptions you control, not forecasts. The value of the exercise is forcing yourself to size the downside before you fall in love with the upside. In memory, both tails are fat. A name that has tripled can give back a third of that in a single negative data point about hyperscaler spending, and the scenario sheet makes that arithmetic concrete in dollars rather than hand-waving.
Reading the Momentum and Technicals
After a half-year of vertical moves, the most practical question is rarely "is this a good company" but "how stretched is the chart." The Momentum and Technicals sheet pulls the trend and timing read for every name:
50-day SMA =SimpleMovingAverage("MU",50)
200-day SMA =SimpleMovingAverage("MU",200)
% vs 200-day SMA =PercentChangeFrom200_DayMovingAverage("MU")
RSI =RSI("MU")
% below 52-week high =PercentBelowFiftyTwoWeekHigh("MU")
The educational rules of thumb are unchanged in any market. Price above a rising 50-day and 200-day average is a healthy uptrend. RSI above 70 marks an overbought, extended condition that can persist longer than skeptics expect but raises the odds of a pullback. A name sitting 40 percent above its 200-day average has priced in a great deal of good news. None of this is a buy or sell trigger by itself; it is context to weigh against the fundamentals on the screener sheet. Technicals tell you about positioning and crowd psychology, not about whether a business is sound.
Position Sizing for High-Beta Names
Memory stocks carry betas well above the market, which means a portfolio that looks balanced by name count can be wildly unbalanced by risk. The Portfolio Allocation sheet starts from three inputs you set: portfolio size, maximum position percent, and the number of names you want to hold. It then computes an equal-weight dollar allocation, the percent that represents, and a version capped at your maximum position size.
Equal weight $ =PortfolioSize / NumberOfNames
Capped position =MIN(EqualWeight, PortfolioSize * MaxPosition%)
The lesson the sheet is designed to teach is restraint. When a single high-beta name has run several hundred percent, it can quietly become the largest source of risk in a portfolio even if it is a modest share of capital. Capping position size by a hard rule, decided in advance, is one of the few defenses against letting a winner turn into an outsized liability. This is mechanical bookkeeping, not advice about how much you personally should own.
Peer Comparison: Where Value Still Lives
The Peer Comparison sheet lines all twelve names up on forward P/E, price to sales, revenue growth, operating margin, return on equity, and year-to-date change, with color coding to spot the standouts. The recurring pattern in memory is a trade-off: the cheapest names on forward earnings are usually the most cyclical price-takers, while the names with the fattest, steadiest margins are the equipment and IP suppliers that command higher multiples for that stability.
That trade-off is the entire strategic question for the group. Do you want maximum leverage to the cycle through a pure-play that is cheap on this year's peak earnings but exposed if the cycle turns? Or do you want the smoother ride of a supplier that participates in every memory build-out but never spikes the way a price-taker can? The grid does not answer that for you. It just makes the choice legible so you can decide with your eyes open. As always, this is a framework for analysis, not a recommendation about any specific name.
Why an Excel Screener Beats a Web Dashboard Here
Free web screeners are fine for a quick look, but a memory cycle rewards a tool you can bend to your own thinking. In Excel with MarketXLS you control the universe, the metrics, the scoring weights and the scenarios. You can add a ticker the moment a new memory name lists, fold in a balance-sheet factor, or rewrite the score entirely. The data refreshes live, so the same workbook you build today is still current next quarter. And because it is your file, your assumptions and your notes travel with it. That combination of live data and full control is the reason advisors and self-directed investors keep their real work in spreadsheets. You can see the broader toolkit on the MarketXLS site or book a demo to see the memory functions run live.
Download the Templates
Download the templates:
- - Pre-filled with illustrative June 2026 data so you can explore the layout immediately
- - Live-updating formulas that refresh when MarketXLS is installed
Both files share the same six-sheet structure: How To Use, Memory Chip Screener, Scenario Analysis, Momentum and Technicals, Portfolio Allocation, and Peer Comparison. Every sheet lists the exact MarketXLS functions it uses so you can reference them when building your own work.
Frequently Asked Questions
What are memory chip stocks?
Memory chip stocks are companies whose business is built on making, supplying, or directly benefiting from computer memory: DRAM and NAND flash. The group spans pure-play makers like Micron, storage companies like Western Digital and Seagate, intellectual-property firms like Rambus, and the equipment and materials suppliers such as Lam Research, Applied Materials and Entegris that sell into every memory fab. In 2026 the group also includes the AI accelerator makers whose demand for high-bandwidth memory drives the cycle.
What is HBM and why does it matter for these stocks?
HBM, or high-bandwidth memory, is DRAM stacked vertically and wired with through-silicon vias to deliver far more bandwidth than standard memory. AI accelerators rely on it, and it consumes much more wafer capacity per bit than commodity DRAM. When makers shift capacity to HBM, conventional DRAM supply tightens too, which is why HBM demand has lifted the entire memory complex in 2026.
Which MarketXLS formulas does the screener use?
The screener uses functions including =QM_Last, =MarketCapitalization, =PERatio, =ForwardPE, =PriceToSales, =GrossMargin, =OperatingMargin, =RevenueGrowth, =ReturnOnEquity, =Beta, =RSI, =SimpleMovingAverage, =PercentChangeFrom200_DayMovingAverage, =PercentBelowFiftyTwoWeekHigh and =FiftyTwoWeekChange. Every formula in the template is a verified MarketXLS function, not a placeholder.
Is the memory cycle near a peak in 2026?
No one knows, and this template does not try to call the top. Memory is famously cyclical, and the same operating leverage that produces enormous gains on the way up produces sharp declines on the way down. The screener is built to monitor the signals that tend to precede a turn, such as decelerating revenue growth, falling margins and extreme price extension, so you can watch the data rather than guess.
How often does the live template update?
The formula version refreshes whenever the workbook recalculates with MarketXLS installed and logged in, so prices, multiples and momentum readings stay current. The sample version holds static illustrative values dated June 29, 2026 so you can explore the structure without an install.
Can I add my own tickers to the screener?
Yes. Add a row, type the ticker once in the first column, and copy the formula columns down. Because every metric references the ticker cell, the entire row populates automatically. You can also re-weight the Memory Score or add new factor columns; the scoring logic lives in plain, editable cells.
The Bottom Line
Memory chip stocks screener excel turns the loudest theme of 2026 into something you can actually analyze instead of chase. The AI memory boom has produced extraordinary returns and equally extraordinary valuations, and the only honest way to navigate that is with a tool that shows momentum, quality, valuation and downside scenarios side by side. The attached template does exactly that across twelve memory and memory-levered names, with live MarketXLS formulas so it stays current as the cycle evolves. Use it as a framework for your own research, not as a substitute for it, and remember that nothing here is investment advice. To build screeners like this with live data across thousands of tickers, explore MarketXLS or book a demo.
