CGNX vs CLIR

Cognex Corporation vs ClearSign Technologies Corporat — Valuation Comparison 2026

CGNX

Industrial Instruments For Measurement, Display, and Control
Cognex Corporation
Quality
9.0
out of 10
Value Trap
33
LOW
Price
$65.85
Last close
Models
13/13
Active
VS

CLIR

Industrial Instruments For Measurement, Display, and Control
ClearSign Technologies Corporat
Quality
6.0
out of 10
Value Trap
24
SAFE
Price
$4.63
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType CGNX Fair ValueCGNX Upside CLIR Fair ValueCLIR Upside
Bayesian DCF Intrinsic $19.11 -71.0% $2.04 -55.9%
Earnings Power Value Intrinsic $11.26 -82.9% $1.96 -61.8%
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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CGNX vs CLIR — Which Stock Is More Undervalued?

CGNX scores higher with a 9.0/10 quality rating vs CLIR's 6.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Cognex Corporation (CGNX) and ClearSign Technologies Corporat (CLIR) 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.

CGNX currently trades at $65.85 with a QOC of 9.0/10, while CLIR trades at $4.63 with a QOC of 6.0/10.

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).