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The most reliable way to compare memory stocks is to synchronize the date and accounting basis, calculate both current-cycle and normalized valuation, separate qualified output from future capacity, and test whether revenue is coming from higher bit shipments, higher prices, or both. Memory earnings can change dramatically when DRAM and NAND average selling prices move, so a low price-to-earnings multiple during a shortage may not mean a stock is cheap. This framework helps you compare manufacturers without pretending that the available evidence identifies a single best or cheapest security.
The latest company figures cited below run through October 13, 2026. They are issuer-reported results and plans, not independent industry forecasts. Current market prices, market capitalizations and comparable valuation multiples are not established here; you must collect those on the same date before making a ranking.
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What a useful memory-stock comparison must measure
Memory manufacturers sell products whose prices and mix can change faster than factory plans. A comparison that uses only a trailing earnings multiple can therefore mistake peak-cycle profits for a permanent earning power. Use five connected axes:
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|---|---|---|
| Valuation | Equity value, enterprise value, diluted share count, current earnings and free cash flow, plus normalized mid-cycle earnings and free cash flow | Separates the price you pay from unusually high or low points in the cycle |
| Capacity | Installed and producing wafers, qualified bits, ramping lines, process nodes, yields, product mix and packaging throughput | Nominal wafer capacity does not equal sellable DRAM or NAND bits |
| Customer demand | Bit shipments, average selling prices (ASPs), end-market mix, inventories, allocations, qualifications and contract terms | Shows whether demand is durable and whether customers can actually take the output |
| Profit durability | Margins across several quarters, cost reductions, capital intensity and the speed at which competitors can add supply | Tests whether current profitability can survive a pricing reversal |
| Shareholder exposure | Pure-play versus conglomerate structure, listing venue, currency and security type | The equity may provide different memory exposure than the operating segment suggests |
Do not combine one company’s fiscal-year numbers with another company’s quarter, or one company’s consolidated figures with another’s memory segment, without labeling the difference. Keep DRAM and NAND in separate columns throughout the worksheet.
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Set a common date, currency and accounting basis
Freeze the measurement date
Choose a date and use the closing share price, shares outstanding, debt, cash and latest reported financial statements available on that date for every company. Record the fiscal period end separately from the market-price date. A company can report a quarter months before another reports its fiscal year, creating a false valuation or growth advantage if you mix periods.
Match accounting conventions
Identify whether each result uses U.S. GAAP, IFRS or K-IFRS, and whether figures are consolidated or segment-level. Note one-time items, stock compensation, restructuring, impairments and foreign-exchange effects. For a cross-border comparison, translate won, dollars or another reporting currency at a stated exchange-rate date; do not silently use a current rate for historical earnings.
Use the correct share count and security
Market capitalization should use the diluted or otherwise consistently defined share count. For a depositary receipt, verify the conversion ratio and whether the quoted instrument is an ADR, GDR or ordinary share. A listing venue does not by itself establish that an instrument is accessible or suitable for every investor.
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Separate equity value from enterprise value
Equity value is the share price multiplied by the relevant share count. Enterprise value (EV) adds debt and other debt-like claims and subtracts cash and cash equivalents, with any additional adjustments applied consistently. EV is useful when companies have different net-cash or net-debt positions; equity multiples are still relevant to the shareholder who owns the residual claim.
At minimum, calculate:
- Price-to-earnings: equity value divided by net income attributable to common shareholders.
- EV-to-EBIT: EV divided by operating income, with consistent treatment of unusual items.
- EV-to-EBITDA: useful for comparing operating performance, but less informative if depreciation and capital intensity differ substantially.
- Price-to-free-cash-flow or EV-to-free-cash-flow: use a clearly defined cash-flow measure after maintenance and growth capital spending.
- Price-to-book or EV-to-sales: secondary checks when earnings are negative or unusually volatile, never substitutes for a cycle analysis.
State the date, currency, share count, debt and cash included in every multiple. A company with large net cash can have a lower EV multiple than its equity P/E suggests; a heavily investing company can show accounting profit while producing little free cash flow.
Show current-cycle and normalized multiples side by side
Current-cycle earnings answer “what did the company earn in the latest period?” Normalized earnings answer “what might it earn under a defensible mid-cycle price, mix and utilization assumption?” Present both rather than replacing one with the other.
A transparent normalization schedule starts with separate DRAM and NAND revenue. For each, model bits shipped multiplied by an ASP, then subtract cash costs, depreciation, selling and administrative expenses, research and development, taxes and other recurring items. Keep assumptions explicit:
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- ASP relative to the latest reported level, with a downside and upside case.
- Yield and cost per bit by process node.
- Product mix, including high-bandwidth memory (HBM), server DRAM, mobile memory, client products and enterprise or consumer NAND.
- Utilization and the timing of new cleanrooms, tools and packaging lines.
- Maintenance versus expansion capital spending and the working-capital effect of inventory changes.
Use a range of normalized earnings or free cash flow, not a single point that implies false precision. A useful table has a downturn, mid-cycle and strong-cycle column, each tied to an ASP, bit-growth, margin and capital-spending assumption. The resulting multiple is a scenario output, not a market fact.
Check what the multiple is really measuring
A very low P/E can result from temporarily high ASPs, unusually favorable mix or a tax benefit. A high P/E can reflect trough earnings rather than an expensive enterprise. Compare the multiple with gross and operating margins over several years, and ask how quickly industry supply could catch up with demand. Memory companies are capital-intensive; depreciation, tool purchases and construction commitments can turn an apparently attractive EBITDA multiple into weak free cash flow.
Measure capacity as qualified, sellable supply
Use a four-stage capacity pipeline
Classify every capacity statement into one of these stages:
| Stage | Definition | How to count it |
|---|---|---|
| Installed | Equipment and cleanroom space that exist | Do not assume all installed tools are running or qualified |
| Currently producing | Lines making output at the measurement date | Track utilization, yield and product assignment |
| Ramping or qualifying | Production is increasing or customers are still validating the product | Model a time-phased contribution, not full run-rate output |
| Announced or planned | Future cleanrooms, tools or projects that have not begun qualified production | Keep out of current supply; record expected opening and ramp dates |
Track wafer starts, good dies, packaged units and bits where available. A 300-millimeter wafer count is not directly comparable across nodes or products because die size, redundancy, yield and bit density differ. Convert to sellable bits only when the company supplies enough information to do so; otherwise label the metric as nominal wafer capacity.
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Keep DRAM and NAND separate
DRAM and NAND have different process technologies, product cycles, customer qualification requirements and pricing behavior. Within DRAM, distinguish conventional server and client products from HBM, whose advanced packaging and testing can constrain shipments even when wafer capacity is available. Within NAND, distinguish layer count, controller integration, enterprise SSD qualification and client or mobile demand. A company can have ample NAND wafers but insufficient controller, test or packaging capacity for the product customers want.
Account for yield, mix and packaging bottlenecks
Ask whether a reported capacity increase comes from more wafers, better yield, higher bits per wafer, a product conversion or a new site. Higher-value products can raise revenue without proportional bit growth; conversely, converting a line to HBM or another advanced product can reduce commodity output. Record the expected qualification date and the point at which management says meaningful volume will ship.
Read investment phasing as a supply signal
Separate committed capital spending from optional projects. A new cleanroom may open years before tools are installed, and a tool order may precede customer qualification. If management says later phases depend on customer demand or investment efficiency, treat that as a conditional plan. Planned supply can still pressure future ASPs, but it is not current output.
Test whether customer demand is high quality
Decompose revenue into bits, prices and mix
For each memory category, compare year-over-year bit shipments with ASP change. Revenue growth driven mostly by ASP is more exposed to a later price reversal than growth driven by sustained bit demand, although a mix shift into HBM or enterprise products can improve economics. Use a bridge such as:
Revenue change ≈ bit-volume effect + ASP effect + product-mix effect + currency effect
Do not treat this approximation as audited accounting. It is a diagnostic that forces you to ask what actually changed.
Map end markets and customer concentration
Separate AI and data-center demand from mobile, client PC, automotive, industrial, networking and other markets. AI-related demand can support premium products, but it does not eliminate the possibility that standard DRAM or NAND enters oversupply. Check the share of revenue from the largest customers, qualification status, design wins and whether a customer can switch suppliers.
Read contracts by their terms, not their headline
Multi-year or take-or-pay arrangements can improve volume visibility. Examine minimum and maximum volumes, fixed or periodically negotiated prices, pricing bands, termination rights, quality and delivery conditions, credit exposure and the customer’s ability to defer purchases. A contract does not make earnings immune to the spot market when prices are fixed only within a band or renegotiated periodically.
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Compare company inventory days, customer inventory commentary, distributor stocks and allocation language. Tight allocation with customers requesting more product is different from shipments made into a channel that is already full. Qualification progress and timely delivery can be more informative than a broad statement that demand is “strong.”
What Micron’s FY2026 figures show—and what they do not prove
Micron’s fiscal-year 2026 Form 10-K reports that AI-driven memory and storage demand outpaced industry supply and describes allocation decisions that could affect customers and end markets. The filing is available from the U.S. Securities and Exchange Commission. Treat these as Micron’s company-level disclosures, not independent industry statistics.
Micron reported that FY2026 DRAM sales increased 252%, primarily reflecting an approximately 180% increase in ASP and a bit-shipment increase in the mid-20% range. NAND sales increased 274%, primarily reflecting approximately 200% ASP growth and a bit-shipment increase also in the mid-20% range. The comparison is year over year for Micron’s fiscal year; it is not a forecast or a market-wide measure. The decomposition illustrates why an investor must not read sales growth as equivalent to bit growth.
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Consolidated gross margin was 81% in FY2026, versus 40% in FY2025 and 22% in FY2024. Such a sharp change is precisely why normalized scenarios should use less favorable prices and margins than the latest peak period. A current-cycle earnings multiple based on an 81% gross margin could look inexpensive even if margins later return toward a lower mid-cycle level.
Micron’s Cloud Memory Business Unit revenue was $43.09 billion in FY2026, compared with $13.52 billion in FY2025. Core Data Center Business Unit revenue was $37.59 billion, compared with $7.23 billion in FY2025. These figures demonstrate the company’s mix and demand exposure during the reported year; they do not establish that the same growth rate will persist.
“The AI-driven growth in the data center has accelerated demand for memory and storage at a rate greater than our ability and the industry’s ability to increase supply.”
This is Micron’s statement in its FY2026 Form 10-K. Use it as management’s characterization of conditions, alongside shipment, ASP, inventory and competitor data.
The filing also describes multi-year strategic customer agreements with enforceable volume commitments and pricing that is fixed or periodically negotiated. Most agreements have minimum and maximum pricing bands. When modeling them, apply the stated bands, volumes and customer-performance conditions rather than treating contracted revenue as guaranteed at the highest possible price.
What SK hynix’s Q2 2026 release adds
SK hynix reported Q2 2026 revenue of 79.3187 trillion won and operating profit of 60.5426 trillion won under K-IFRS, with a 76% operating margin versus 41% in Q2 2025. The figures come from the company’s July 29, 2026 release, “SK hynix Announces 2Q26 Financial Results.” The release says the results are preliminary and may change after independent audit; preserve that qualification in any model or quotation.
Management attributed the result to AI-infrastructure demand and high-performance products. It said long-term agreements had been finalized with around 10 customers and that discussions with major industry clients continued. The number of agreements is a company statement, not proof that all future volume or pricing is fixed.
SK hynix said HBM4 mass shipments began in Q2 2026 and production would ramp in the second half of the year. It also said 321-layer NAND represented the largest share of production and that it planned to expand the technology to approximately 50% of domestic production capacity by year-end. These statements should be entered as product and ramp milestones, with dates and execution risk, not as already-achieved full-year capacity.
The company described an accelerated M15X mass-production schedule and expected the Yongin Phase 1 cleanroom to open in early 2027. Longer-term investments, including P&T7, M17 NAND and a new semiconductor cluster, were described as phased according to customer demand and investment efficiency. Keep each project in the planned or ramping category until qualified output is disclosed.
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“In a market environment where customer demand exceeds supply capabilities, the ability to deliver requested volumes in a timely manner has emerged as a core business competitiveness.”
This sentence reflects SK hynix’s stated view in the July 29, 2026 release. Test it against actual shipments, ASPs, allocation disclosures and customer qualifications.
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SK hynix identifies common shares traded on the Korea Exchange, American Depositary Shares on NASDAQ and Global Depository Shares on the Luxembourg Stock Exchange. Compare the specific instrument’s conversion ratio, liquidity, currency and investor access before treating the listings as interchangeable.
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Populate a worksheet with the same fields for every manufacturer. A blank or unavailable field is more honest than an invented estimate.
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| Category | Micron example from disclosed evidence | SK hynix example from disclosed evidence | How to use it |
|---|---|---|---|
| Reporting period | FY2026; fiscal year | Q2 2026; preliminary K-IFRS quarter | Do not compare directly without aligning periods |
| DRAM revenue change | Sales increased 252%; ASP about +180%; bits mid-20% range | Not stated in the cited release | Use the decomposition only where the issuer reports the components |
| NAND revenue change | Sales increased 274%; ASP about +200%; bits mid-20% range | 321-layer products were the largest production share; no comparable revenue bridge stated | Keep production mix separate from revenue growth |
| Reported profitability | Consolidated gross margin 81% in FY2026, 40% in FY2025, 22% in FY2024 | Operating margin 76% in Q2 2026 versus 41% in Q2 2025 | Use multi-period normalization and preserve accounting differences |
| AI/data-center evidence | Cloud Memory Business Unit revenue $43.09 billion; Core Data Center Business Unit $37.59 billion in FY2026 | Management cited AI infrastructure demand and high-performance products | These are company-specific disclosures, not independent market totals |
| Future capacity | Not stated in the cited figures | M15X schedule, Yongin Phase 1 early-2027 cleanroom, P&T7, M17 and cluster plans | Keep announced projects out of current qualified output |
| Contracts | Multi-year agreements with enforceable volumes and fixed or periodically negotiated pricing, generally within bands | Long-term agreements finalized with around 10 customers; further talks ongoing | Model volume, price bands and conditions individually |
| Current stock valuation | Not established in the cited evidence | Not established in the cited evidence | Collect same-date prices, shares, debt and cash yourself |
Run a three-scenario cycle stress test
Downside case
Reduce DRAM and NAND ASPs, slow bit growth, lower utilization and preserve committed capital spending. Include inventory write-down or working-capital pressure only if your accounting treatment is explicit. Test whether the company remains cash-generative after essential maintenance spending and whether debt or new equity could become necessary.
Mid-cycle case
Use a multi-year average or a deliberately conservative ASP and gross-margin assumption, normal utilization and a realistic product mix. Include the capital required to maintain process leadership and packaging capacity. This is the case that should anchor a normalized EV/earnings or EV/free-cash-flow comparison.
Strong-cycle case
Allow higher ASPs, stronger mix and better utilization, but cap the scenario with plausible capacity additions and customer absorption. A strong-cycle case is useful for understanding upside sensitivity, not for proving that a low multiple is safe.
For each case, record revenue, gross margin, operating income, taxes, capital spending, free cash flow, net cash or debt and diluted shares. Then calculate the implied multiple from the same market date. If a company’s apparent advantage disappears in the mid-cycle or downside case, the valuation is cycle-dependent rather than structurally cheap.
Common comparison errors and how to correct them
Ranking on a single trailing P/E
Problem: Peak ASPs make earnings look unusually large.
Correction: Show current and normalized earnings, with a downside case tied to explicit ASP and margin assumptions.
Counting announced cleanrooms as current supply
Problem: A planned facility is treated as if it were shipping qualified bits.
Correction: Place projects in a dated pipeline and move them to current capacity only after production and qualification evidence.
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Problem: Wafer starts ignore node density, yield, die size, product conversion and packaging.
Correction: Track good dies, packaged units or bits where disclosed; otherwise label the figure nominal and avoid a precise revenue conversion.
Treating AI demand as universal memory demand
Problem: Data-center HBM strength is extrapolated to all DRAM and NAND.
Correction: Split products and end markets, then examine standard-memory pricing, inventories and allocations.
Assuming contracts eliminate the spot cycle
Problem: A multi-year agreement is modeled at a fixed high price with no conditions.
Correction: Read volume commitments, price bands, renegotiation points, delivery obligations and customer credit terms.
Mixing currencies, periods or listing types
Problem: A won-denominated quarterly result is compared with a dollar fiscal year and an unadjusted depositary receipt.
Correction: Align the date, accounting basis, currency conversion, share count and depositary ratio before calculating multiples.
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Problem: The memory segment’s performance is treated as the entire equity’s exposure.
Correction: Account for non-memory businesses, holding-company discounts, cross-shareholdings and the proportion of group earnings tied to memory. The available evidence does not quantify that difference, so do not assign a numerical premium or discount without additional data.
A repeatable worksheet for comparing memory manufacturers
- Choose the date. Write down the market close and the latest financial-report date used for every company.
- Identify the security. Record exchange, ticker, share class, depositary ratio and currency.
- Collect capital structure data. Capture diluted shares, debt, leases or other debt-like claims, cash and investments using consistent definitions.
- Calculate equity value and EV. Keep the arithmetic visible so net cash or debt cannot be overlooked.
- Build the earnings bridge. Separate DRAM and NAND revenue, bits, ASPs, mix and currency effects.
- Normalize profitability. Model downside, mid-cycle and strong-cycle ASPs, margins, utilization, taxes and capital spending.
- Map qualified capacity. Classify installed, producing, ramping and planned output; record node, yield, product and packaging constraints.
- Score demand quality. Review end markets, customer concentration, inventory, allocations, qualifications and contract terms.
- Stress the supply response. Estimate when announced projects and competitor additions could reach qualified output and pressure prices.
- Compare scenarios, not slogans. Rank the securities only after checking normalized EV and equity multiples, free cash flow, balance-sheet resilience and shareholder exposure.
How to interpret the final comparison
A manufacturer with the fastest reported growth is not automatically the best investment exposure. Ask which part of the result is structural: process leadership, yield, packaging, customer qualification, cost per bit or a durable contractual relationship. Then ask which part is cyclical: ASP, utilization, temporary allocation and unusually favorable product mix.
Favor a conclusion that remains understandable when the cycle turns. It should state the market date, valuation method, normalization assumptions, capacity stage and demand evidence. If the data cannot support a same-date comparison, the correct conclusion is that the stocks are not yet ranked—not that one is cheap by default.
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