NTZ vs SGI

Natuzzi, S.p.A. vs Somnigroup International Inc. — Valuation Comparison 2026

NTZ

Furnishings, Fixtures & Appliances
Natuzzi, S.p.A.
Quality
1.7
out of 10
Value Trap
Price
$2.35
Last close
Models
10/13
Active
VS

SGI

Furnishings, Fixtures & Appliances
Somnigroup International Inc.
Quality
8.7
out of 10
Value Trap
39
LOW
Price
$71.36
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType NTZ Fair ValueNTZ Upside SGI Fair ValueSGI Upside
Bayesian DCF Intrinsic $0.50 -78.8% $3.29 -95.4%
Earnings Power Value Intrinsic $0.91 -98.7%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $2.42 -7.0% $46.73 -34.5%
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 NTZ vs SGI — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

NTZ vs SGI — Which Stock Is More Undervalued?

SGI scores higher with a 8.7/10 quality rating vs NTZ's 1.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Natuzzi, S.p.A. (NTZ) and Somnigroup International Inc. (SGI) 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.

NTZ currently trades at $2.35 with a QOC of 1.7/10, while SGI trades at $71.36 with a QOC of 8.7/10.

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).