LKQ vs MOD

LKQ Corporation vs Modine Manufacturing Company — Valuation Comparison 2026

LKQ

Auto Parts
LKQ Corporation
Quality
8.6
out of 10
Value Trap
6
SAFE
Price
$27.29
Last close
Models
12/13
Active
VS

MOD

Auto Parts
Modine Manufacturing Company
Quality
8.1
out of 10
Value Trap
6
SAFE
Price
$270.70
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType LKQ Fair ValueLKQ Upside MOD Fair ValueMOD Upside
Bayesian DCF Intrinsic $30.52 +11.8% $6.21 -97.7%
Earnings Power Value Intrinsic $12.70 -53.5% $24.52 -90.9%
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 LKQ vs MOD — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

LKQ vs MOD — Which Stock Is More Undervalued?

LKQ scores higher with a 8.6/10 quality rating vs MOD's 8.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing LKQ Corporation (LKQ) and Modine Manufacturing Company (MOD) 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.

LKQ currently trades at $27.29 with a QOC of 8.6/10, while MOD trades at $270.70 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).