CODI vs NTZ

D/B/A Compass Diversified Holdi vs Natuzzi, S.p.A. — Valuation Comparison 2026

CODI

Household Furniture
D/B/A Compass Diversified Holdi
Quality
5.4
out of 10
Value Trap
37
LOW
Price
$11.35
Last close
Models
10/13
Active
VS

NTZ

Household Furniture
Natuzzi, S.p.A.
Quality
1.7
out of 10
Value Trap
Price
$2.18
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType CODI Fair ValueCODI Upside NTZ Fair ValueNTZ Upside
Bayesian DCF Intrinsic $0.56 -74.1%
Earnings Power Value Intrinsic $1.51 -86.9%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $0.98 -91.4% $2.42 -7.0%
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 CODI vs NTZ — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

CODI vs NTZ — Which Stock Is More Undervalued?

CODI scores higher with a 5.4/10 quality rating vs NTZ's 1.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing D/B/A Compass Diversified Holdi (CODI) and Natuzzi, S.p.A. (NTZ) 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.

CODI currently trades at $11.35 with a QOC of 5.4/10, while NTZ trades at $2.18 with a QOC of 1.7/10.

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).