ATMU vs DAN

Atmus Filtration Technologies I vs Dana Incorporated — Valuation Comparison 2026

ATMU

Motor Vehicle Parts & Accessories
Atmus Filtration Technologies I
Quality
9.8
out of 10
Value Trap
Price
$46.78
Last close
Models
12/13
Active
VS

DAN

Motor Vehicle Parts & Accessories
Dana Incorporated
Quality
6.5
out of 10
Value Trap
20
SAFE
Price
$35.41
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType ATMU Fair ValueATMU Upside DAN Fair ValueDAN Upside
Bayesian DCF Intrinsic $10.23 -78.1% $9.49 -73.2%
Earnings Power Value Intrinsic $12.74 -72.8% $8.52 -75.9%
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 $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

ATMU vs DAN — Which Stock Is More Undervalued?

ATMU scores higher with a 9.8/10 quality rating vs DAN's 6.5/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Atmus Filtration Technologies I (ATMU) and Dana Incorporated (DAN) 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.

ATMU currently trades at $46.78 with a QOC of 9.8/10, while DAN trades at $35.41 with a QOC of 6.5/10.

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).