CTRN vs GAP

Citi Trends, Inc. vs Gap, Inc. (The) — Valuation Comparison 2026

CTRN

Apparel Retail
Citi Trends, Inc.
Quality
5.8
out of 10
Value Trap
6
SAFE
Price
$50.50
Last close
Models
11/13
Active
VS

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

Model-by-Model Comparison

ModelType CTRN Fair ValueCTRN Upside GAP Fair ValueGAP Upside
Bayesian DCF Intrinsic $1.59 -96.8% $8.94 -64.2%
Earnings Power Value Intrinsic $46.59 -2.8% $13.74 -45.0%
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 CTRN vs GAP — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

CTRN vs GAP — Which Stock Is More Undervalued?

GAP scores higher with a 8.4/10 quality rating vs CTRN's 5.8/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Citi Trends, Inc. (CTRN) and Gap, Inc. (The) (GAP) 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.

CTRN currently trades at $50.50 with a QOC of 5.8/10, while GAP trades at $25.00 with a QOC of 8.4/10.

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).