BN · rank #7 · 2026-07-22
TER
NASDAQ · $52.25B (USD)
The exact numbers the algorithm saw.
| Composite score Z-score blend of the factors below; drives the rank. | 0.52 |
|---|---|
| Forward revenue growth Consensus forward revenue growth. | +24.2% |
| Forward net margin Consensus forward net margin. | +24.7% |
| Net margin TTM Trailing twelve month net margin. | +22.6% |
| 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. | 1.32 |
| Debt / FCF Net debt relative to free cash flow. Lower is safer. | 0.15× |
| Analyst upside Spread between the consensus 12m target and the current price. | +14.0% |
| Last EPS surprise Most recent reported EPS versus consensus. | +21% |
| Market cap (USD) | $52.25B |
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
Teradyne is the dominant supplier of automated test equipment for advanced semiconductors, with ~70% of Q1 2026 revenue tied to AI-related compute testing and a record $1.282B quarter driven by surging demand for high-end chip validation.
Rationale
TER is a textbook bottleneck-solver — advanced AI chips (HBM, leading-edge logic) cannot ramp to volume production without passing through Teradyne's test platforms, and management's evergreen ATE TAM model of $12–$14B with explicit margin targets signals structural, not cyclical, revenue visibility.
Material risks
- 1Customer concentration at Qualcomm and Texas Instruments creates binary exposure — if either accelerates in-house test capability, TER loses captive demand that underpins the bottleneck thesis.
- 2Q2 2026 earnings land July 28 (6 days post as-of-date), and guidance of $1.15–$1.25B implies sequential deceleration from the record Q1, creating a near-term catalyst risk where any miss or cautious H2 commentary could reprice the stock sharply before the position matures.
AI verdict council
Each pick is reviewed independently by 3 models before any order. 2 of 3 voted to proceed.