GAP vs LULU

Gap, Inc. (The) vs lululemon athletica inc. — Valuation Comparison 2026

GAP

Apparel Retail
Gap, Inc. (The)
Quality
8.4
out of 10
Value Trap
6
SAFE
Price
$25.00
Last close
Models
13/13
Active
VS

LULU

Apparel Retail
lululemon athletica inc.
Quality
9.1
out of 10
Value Trap
6
SAFE
Price
$131.33
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType GAP Fair ValueGAP Upside LULU Fair ValueLULU Upside
Bayesian DCF Intrinsic $8.94 -64.2% $132.97 +1.2%
Earnings Power Value Intrinsic $13.74 -45.0% $87.07 -33.7%
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 GAP vs LULU — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

GAP vs LULU — Which Stock Is More Undervalued?

LULU scores higher with a 9.1/10 quality rating vs GAP's 8.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Gap, Inc. (The) (GAP) and lululemon athletica inc. (LULU) 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.

GAP currently trades at $25.00 with a QOC of 8.4/10, while LULU trades at $131.33 with a QOC of 9.1/10.

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).