BN · rank #5 · 2026-07-23
LRCX
NASDAQ · $399.29B (USD)
The exact numbers the algorithm saw.
| Composite score Z-score blend of the factors below; drives the rank. | 0.77 |
|---|---|
| Forward revenue growth Consensus forward revenue growth. | +33.1% |
| Forward net margin Consensus forward net margin. | +33.1% |
| Net margin TTM Trailing twelve month net margin. | +30.9% |
| Margin expansion Forward minus trailing net margin (percentage points). | +2.2% |
| Forward PEG Forward P/E to growth. Below 1 is cheap for the growth. | 1.86 |
| Debt / FCF Net debt relative to free cash flow. Lower is safer. | 0.58× |
| Analyst upside Spread between the consensus 12m target and the current price. | +15.3% |
| Last EPS surprise Most recent reported EPS versus consensus. | +8% |
| Market cap (USD) | $399.29B |
The AI research card
Independent qualitative review of each pick before the order is placed. On the rare day the research service is unavailable, the paper book trades on the quant ranking alone and no card appears here.
Summary
Lam Research is a leading wafer fabrication equipment supplier specializing in etch and deposition tools that are critical bottlenecks for producing advanced DRAM, NAND, and logic chips powering AI infrastructure.
Rationale
Record Q3 revenue of $5.84B, Q4 guidance of up to $7.0B, explicit AI-driven demand commentary, 33% forward revenue growth, and high switching costs in specialized etch process IP directly reinforce the bottleneck-solver thesis with multi-quarter revenue visibility rather than sentiment rotation.
Material risks
- 1Escalating U.S. export controls on semiconductor equipment to China could materially restrict shipments to a significant revenue contributor, compressing near-term growth and undermining the visibility that anchors the bottleneck-solver edge.
- 2AI memory architecture shifts — such as adoption of process technologies requiring fewer Lam-intensive etch steps — could erode Lam's sole-source positioning in critical process nodes even if aggregate chip demand remains robust.
AI verdict council
Each pick is reviewed independently by 3 models before any order. 2 of 3 voted to proceed.