MATH vs MC

Metalpha Technology Holding Lim vs Moelis & Company — Valuation Comparison 2026

MATH

Capital Markets
Metalpha Technology Holding Lim
Quality
1.9
out of 10
Value Trap
Price
$1.05
Last close
Models
10/13
Active
VS

MC

Capital Markets
Moelis & Company
Quality
6.9
out of 10
Value Trap
6
SAFE
Price
$66.85
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType MATH Fair ValueMATH Upside MC Fair ValueMC Upside
Bayesian DCF Intrinsic $0.28 -73.5% $88.02 +31.7%
Earnings Power Value Intrinsic $13.83 -79.3%
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 $0.12 -86.4% $11.11 -83.4%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

MATH vs MC — Which Stock Is More Undervalued?

MC scores higher with a 6.9/10 quality rating vs MATH's 1.9/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Metalpha Technology Holding Lim (MATH) and Moelis & Company (MC) 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.

MATH currently trades at $1.05 with a QOC of 1.9/10, while MC trades at $66.85 with a QOC of 6.9/10.

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).