SDM vs STFS

Smart Digital Group Limited vs Star Fashion Culture Holdings L — Valuation Comparison 2026

SDM

Advertising Agencies
Smart Digital Group Limited
Quality
2.0
out of 10
Value Trap
Price
$1.85
Last close
Models
12/13
Active
VS

STFS

Advertising Agencies
Star Fashion Culture Holdings L
Quality
4.4
out of 10
Value Trap
Price
$10.81
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType SDM Fair ValueSDM Upside STFS Fair ValueSTFS Upside
Bayesian DCF Intrinsic $0.48 -73.9% $2.70 -75.1%
Earnings Power Value Intrinsic $0.36 -80.4% $0.18 -95.6%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
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 SDM vs STFS — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

SDM vs STFS — Which Stock Is More Undervalued?

STFS scores higher with a 4.4/10 quality rating vs SDM's 2.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Smart Digital Group Limited (SDM) and Star Fashion Culture Holdings L (STFS) 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.

SDM currently trades at $1.85 with a QOC of 2.0/10, while STFS trades at $10.81 with a QOC of 4.4/10.

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).