FUTU vs GRAN

Futu Holdings Limited vs Grande Group Limited — Valuation Comparison 2026

FUTU

Capital Markets
Futu Holdings Limited
Quality
10.0
out of 10
Value Trap
Price
$104.91
Last close
Models
12/13
Active
VS

GRAN

Capital Markets
Grande Group Limited
Quality
8.6
out of 10
Value Trap
Price
$1.06
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType FUTU Fair ValueFUTU Upside GRAN Fair ValueGRAN Upside
Bayesian DCF Intrinsic $251.49 +139.7% $0.40 -62.7%
Earnings Power Value Intrinsic $144.62 +37.8% $0.85 -19.6%
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 FUTU vs GRAN — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

FUTU vs GRAN — Which Stock Is More Undervalued?

FUTU scores higher with a 10.0/10 quality rating vs GRAN's 8.6/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Futu Holdings Limited (FUTU) and Grande Group Limited (GRAN) 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.

FUTU currently trades at $104.91 with a QOC of 10.0/10, while GRAN trades at $1.06 with a QOC of 8.6/10.

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).