CVGI vs DCH

Commercial Vehicle Group, Inc. vs Dauch Corporation — Valuation Comparison 2026

CVGI

Auto Parts
Commercial Vehicle Group, Inc.
Quality
6.9
out of 10
Value Trap
24
SAFE
Price
$5.25
Last close
Models
12/13
Active
VS

DCH

Auto Parts
Dauch Corporation
Quality
6.9
out of 10
Value Trap
12
SAFE
Price
$6.77
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType CVGI Fair ValueCVGI Upside DCH Fair ValueDCH Upside
Bayesian DCF Intrinsic $11.00 +109.6% $5.77 -14.7%
Earnings Power Value Intrinsic $2.28 -45.0% $3.31 -50.1%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
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 CVGI vs DCH — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

CVGI vs DCH — Which Stock Is More Undervalued?

CVGI scores higher with a 6.9/10 quality rating vs DCH's 6.9/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Commercial Vehicle Group, Inc. (CVGI) and Dauch Corporation (DCH) 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.

CVGI currently trades at $5.25 with a QOC of 6.9/10, while DCH trades at $6.77 with a QOC of 6.9/10.

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).