POAS vs ST

Phaos Technology Holdings (Caym vs Sensata Technologies Holding pl — Valuation Comparison 2026

POAS

Industrial Instruments For Measurement, Display, and Control
Phaos Technology Holdings (Caym
Quality
4.4
out of 10
Value Trap
Price
$0.52
Last close
Models
6/13
Active
VS

ST

Industrial Instruments For Measurement, Display, and Control
Sensata Technologies Holding pl
Quality
6.6
out of 10
Value Trap
25
LOW
Price
$49.39
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType POAS Fair ValuePOAS Upside ST Fair ValueST Upside
Bayesian DCF Intrinsic $0.69 +31.9% $20.85 -57.8%
Earnings Power Value Intrinsic $1.68 -96.6%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $2.68 +413.7% $54.72 +10.8%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
RCMH-DCF Intrinsic $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

Access all valuation models for POAS vs ST — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

POAS vs ST — Which Stock Is More Undervalued?

ST scores higher with a 6.6/10 quality rating vs POAS's 4.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Phaos Technology Holdings (Caym (POAS) and Sensata Technologies Holding pl (ST) across 13 institutional-grade valuation models reveals how each company's intrinsic value stacks up against its market price. CirclFi's engine processes SEC EDGAR 10-K and 10-Q filings, FRED macroeconomic data, and GDELT news sentiment to generate independent fair value estimates daily.

POAS currently trades at $0.52 with a QOC of 4.4/10, while ST trades at $49.39 with a QOC of 6.6/10.

Both companies are analyzed with models spanning intrinsic (Bayesian DCF, EPV), scenario-based (First Chicago), regime-switching (Markov DDM, RCMH-DCF), machine learning (ML-RIV, FTNN Topology), and ensemble methods (CUCE).