FIGS vs GOOS

FIGS, Inc. vs Canada Goose Holdings Inc. Subo — Valuation Comparison 2026

FIGS

Apparel Manufacturing
FIGS, Inc.
Quality
9.2
out of 10
Value Trap
6
SAFE
Price
$12.26
Last close
Models
13/13
Active
VS

GOOS

Apparel Manufacturing
Canada Goose Holdings Inc. Subo
Quality
8.3
out of 10
Value Trap
24
SAFE
Price
$9.98
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType FIGS Fair ValueFIGS Upside GOOS Fair ValueGOOS Upside
Bayesian DCF Intrinsic $4.44 -63.8% $16.93 +69.6%
Earnings Power Value Intrinsic $1.22 -90.0% $15.53 +55.6%
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 FIGS vs GOOS — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

FIGS vs GOOS — Which Stock Is More Undervalued?

FIGS scores higher with a 9.2/10 quality rating vs GOOS's 8.3/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing FIGS, Inc. (FIGS) and Canada Goose Holdings Inc. Subo (GOOS) 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.

FIGS currently trades at $12.26 with a QOC of 9.2/10, while GOOS trades at $9.98 with a QOC of 8.3/10.

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).