BN · rank #8 · 2026-07-21
TER
NASDAQ · $52.25B (USD)
The exact numbers the algorithm saw.
| Composite score Z-score blend of the factors below; drives the rank. | 0.60 |
|---|---|
| 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. | 1.28 |
| 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. | +27.7% |
| 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 directly to AI-related compute testing and a record $1.282B quarter driven by AI chip validation demand.
Rationale
TER is a textbook bottleneck-solver — high-end ATE is a non-negotiable gate in ramping advanced AI processors, management's evergreen model targets $6B revenue against a $12–14B ATE TAM with 59–61% gross margins, and a 20.8% EPS beat with 24% forward revenue growth confirms the structural demand signal is real, not rotational.
Material risks
- 1Customer concentration at Qualcomm and Texas Instruments creates binary exposure — if either accelerates in-house test capability, TER loses a large-revenue sole-source relationship that underpins the bottleneck thesis.
- 2Q2 2026 earnings land July 28 (7 days post as-of-date), and guidance of $1.15–$1.25B implies sequential deceleration from the $1.282B Q1 record, creating near-term EPS risk that could reprice the stock before the position matures.
AI verdict council
Each pick is reviewed independently by 3 models before any order. 2 of 3 voted to proceed.