SUIG vs V

Sui Group Holdings Limited vs Visa Inc. — Valuation Comparison 2026

SUIG

Credit Services
Sui Group Holdings Limited
Quality
3.3
out of 10
Value Trap
12
SAFE
Price
$1.54
Last close
Models
10/13
Active
VS

V

Credit Services
Visa Inc.
Quality
9.5
out of 10
Value Trap
14
SAFE
Price
$324.95
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType SUIG Fair ValueSUIG Upside V Fair ValueV Upside
Bayesian DCF Intrinsic $0.45 -71.1% $195.05 -40.0%
Earnings Power Value Intrinsic $92.75 -71.5%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $0.84 -45.3%
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 SUIG vs V — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

SUIG vs V — Which Stock Is More Undervalued?

V scores higher with a 9.5/10 quality rating vs SUIG's 3.3/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Sui Group Holdings Limited (SUIG) and Visa Inc. (V) 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.

SUIG currently trades at $1.54 with a QOC of 3.3/10, while V trades at $324.95 with a QOC of 9.5/10.

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).