BN · rank #8 · 2026-09-07
AMAT
NASDAQ · $360.86B (USD)
The exact numbers the algorithm saw.
| Composite score Z-score blend of the factors below; drives the rank. | 0.67 |
|---|---|
| Forward revenue growth Consensus forward revenue growth. | +34.7% |
| Forward net margin Consensus forward net margin. | +32.2% |
| Net margin TTM Trailing twelve month net margin. | +30.0% |
| Margin expansion Forward minus trailing net margin (percentage points). | +2.1% |
| Forward PEG Forward P/E to growth. Below 1 is cheap for the growth. | 0.87 |
| Debt / FCF Net debt relative to free cash flow. Lower is safer. | 1.31× |
| Analyst upside Spread between the consensus 12m target and the current price. | +40.9% |
| Last EPS surprise Most recent reported EPS versus consensus. | +4% |
| Market cap (USD) | $360.86B |
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
Applied Materials is the leading supplier of semiconductor deposition, etch, and process control equipment, now generating record revenues as AI-driven demand for advanced packaging, HBM memory, and 3D chip architectures accelerates capital spending across its customer base.
Rationale
AMAT screens as a genuine bottleneck-solver because its June 2026 production systems for 3D AI chip architectures and its SK hynix HBM partnership confirm sole-source-adjacent positioning at the exact process nodes where chipmakers cannot self-supply, supporting the 34.7% forward revenue growth and 0.87x PEG at scale.
Material risks
- 1Leading-edge customers (TSMC, Samsung, Intel) are expanding internal process R&D capabilities, and any shift toward in-house materials integration or rival tool qualification at HBM/advanced packaging nodes would directly erode AMAT's bottleneck premium.
- 2Semiconductor equipment spending is cyclical and concentrated; a capex pause by one or two hyperscaler-driven foundry customers could compress near-term revenue sharply despite the structural AI narrative, given equipment orders are lumpy and backlog visibility is finite.
AI verdict council
Each pick is reviewed independently by 3 models before any order. 3 of 3 voted to proceed.