NCI vs RL

Neo-Concept International Group vs Ralph Lauren Corporation — Valuation Comparison 2026

NCI

Apparel Manufacturing
Neo-Concept International Group
Quality
7.4
out of 10
Value Trap
8
SAFE
Price
$10.10
Last close
Models
11/13
Active
VS

RL

Apparel Manufacturing
Ralph Lauren Corporation
Quality
8.6
out of 10
Value Trap
18
SAFE
Price
$370.77
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType NCI Fair ValueNCI Upside RL Fair ValueRL Upside
Bayesian DCF Intrinsic $0.59 -94.2% $299.28 -19.3%
Earnings Power Value Intrinsic $116.43 -68.6%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $0.09 -91.4% $79.23 -77.9%
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 NCI vs RL — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

NCI vs RL — Which Stock Is More Undervalued?

RL scores higher with a 8.6/10 quality rating vs NCI's 7.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Neo-Concept International Group (NCI) and Ralph Lauren Corporation (RL) 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.

NCI currently trades at $10.10 with a QOC of 7.4/10, while RL trades at $370.77 with a QOC of 8.6/10.

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).