SENS vs ST

Senseonics Holdings, Inc. vs Sensata Technologies Holding pl — Valuation Comparison 2026

SENS

Industrial Instruments For Measurement, Display, and Control
Senseonics Holdings, Inc.
Quality
6.8
out of 10
Value Trap
30
LOW
Price
$6.79
Last close
Models
11/13
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 SENS Fair ValueSENS Upside ST Fair ValueST Upside
Bayesian DCF Intrinsic $0.98 -85.5% $20.85 -57.8%
Earnings Power Value Intrinsic $1.21 -76.4% $1.68 -96.6%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
RCMH-DCF Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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SENS vs ST — Which Stock Is More Undervalued?

SENS scores higher with a 6.8/10 quality rating vs ST's 6.6/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Senseonics Holdings, Inc. (SENS) 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.

SENS currently trades at $6.79 with a QOC of 6.8/10, 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).