ESAB vs NDSN

ESAB Corporation vs Nordson Corporation — Valuation Comparison 2026

ESAB

General Industrial Machinery & Equipment, NEC
ESAB Corporation
Quality
8.7
out of 10
Value Trap
30
LOW
Price
$92.43
Last close
Models
12/13
Active
VS

NDSN

General Industrial Machinery & Equipment, NEC
Nordson Corporation
Quality
8.1
out of 10
Value Trap
25
LOW
Price
$287.33
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType ESAB Fair ValueESAB Upside NDSN Fair ValueNDSN Upside
Bayesian DCF Intrinsic $19.61 -78.8% $198.05 -31.1%
Earnings Power Value Intrinsic $29.58 -68.0% $70.66 -75.4%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
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 ESAB vs NDSN — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

ESAB vs NDSN — Which Stock Is More Undervalued?

ESAB scores higher with a 8.7/10 quality rating vs NDSN's 8.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing ESAB Corporation (ESAB) and Nordson Corporation (NDSN) 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.

ESAB currently trades at $92.43 with a QOC of 8.7/10, while NDSN trades at $287.33 with a QOC of 8.1/10.

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).