CRI vs DBGI

Carter's, Inc. vs Digital Brands Group, Inc. — Valuation Comparison 2026

CRI

Apparel Retail
Carter's, Inc.
Quality
7.5
out of 10
Value Trap
29
LOW
Price
$40.12
Last close
Models
12/13
Active
VS

DBGI

Apparel Retail
Digital Brands Group, Inc.
Quality
3.3
out of 10
Value Trap
52
WARN
Price
$0.49
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType CRI Fair ValueCRI Upside DBGI Fair ValueDBGI Upside
Bayesian DCF Intrinsic $10.77 -73.2% $0.06 -88.5%
Earnings Power Value Intrinsic $14.19 -64.6% $0.54 -59.6%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 CRI vs DBGI — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

CRI vs DBGI — Which Stock Is More Undervalued?

CRI scores higher with a 7.5/10 quality rating vs DBGI's 3.3/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Carter's, Inc. (CRI) and Digital Brands Group, Inc. (DBGI) 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.

CRI currently trades at $40.12 with a QOC of 7.5/10, while DBGI trades at $0.49 with a QOC of 3.3/10.

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).