LECO vs NNBR

Lincoln Electric Holdings, Inc. vs NN, Inc. — Valuation Comparison 2026

LECO

Metalworkg Machinery & Equipment
Lincoln Electric Holdings, Inc.
Quality
9.7
out of 10
Value Trap
11
SAFE
Price
$258.49
Last close
Models
12/13
Active
VS

NNBR

Metalworkg Machinery & Equipment
NN, Inc.
Quality
5.4
out of 10
Value Trap
26
LOW
Price
$2.97
Last close
Models
8/13
Active

Model-by-Model Comparison

ModelType LECO Fair ValueLECO Upside NNBR Fair ValueNNBR Upside
Bayesian DCF Intrinsic $126.48 -51.1%
Earnings Power Value Intrinsic $61.24 -76.3%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $256.80 -0.7% $4.51 +51.8%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $0.40 -86.5%
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 LECO vs NNBR — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

LECO vs NNBR — Which Stock Is More Undervalued?

LECO scores higher with a 9.7/10 quality rating vs NNBR's 5.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Lincoln Electric Holdings, Inc. (LECO) and NN, Inc. (NNBR) 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.

LECO currently trades at $258.49 with a QOC of 9.7/10, while NNBR trades at $2.97 with a QOC of 5.4/10.

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).