MDBH vs MEGL

MDB Capital Holdings, LLC vs Magic Empire Global Limited — Valuation Comparison 2026

MDBH

Finance Services
MDB Capital Holdings, LLC
Quality
6.0
out of 10
Value Trap
6
SAFE
Price
$3.50
Last close
Models
11/13
Active
VS

MEGL

Finance Services
Magic Empire Global Limited
Quality
5.6
out of 10
Value Trap
35
LOW
Price
$1.20
Last close
Models
9/13
Active

Model-by-Model Comparison

ModelType MDBH Fair ValueMDBH Upside MEGL Fair ValueMEGL Upside
Bayesian DCF Intrinsic $1.65 -52.9% $1.86 +54.9%
Earnings Power Value Intrinsic $1.57 -59.4%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $1.38 -60.5% $1.63 +35.8%
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 $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

Access all valuation models for MDBH vs MEGL — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

MDBH vs MEGL — Which Stock Is More Undervalued?

MDBH scores higher with a 6.0/10 quality rating vs MEGL's 5.6/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing MDB Capital Holdings, LLC (MDBH) and Magic Empire Global Limited (MEGL) 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.

MDBH currently trades at $3.50 with a QOC of 6.0/10, while MEGL trades at $1.20 with a QOC of 5.6/10.

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).