MGN vs MSGY

Megan Holdings Limited vs Masonglory Limited — Valuation Comparison 2026

MGN

Engineering & Construction
Megan Holdings Limited
Quality
5.8
out of 10
Value Trap
Price
$0.16
Last close
Models
12/13
Active
VS

MSGY

Engineering & Construction
Masonglory Limited
Quality
2.2
out of 10
Value Trap
Price
$0.47
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType MGN Fair ValueMGN Upside MSGY Fair ValueMSGY Upside
Bayesian DCF Intrinsic $0.03 -80.6% $0.12 -73.6%
Earnings Power Value Intrinsic $0.02 -86.6% $0.13 -72.0%
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 MGN vs MSGY — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

MGN vs MSGY — Which Stock Is More Undervalued?

MGN scores higher with a 5.8/10 quality rating vs MSGY's 2.2/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Megan Holdings Limited (MGN) and Masonglory Limited (MSGY) 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.

MGN currently trades at $0.16 with a QOC of 5.8/10, while MSGY trades at $0.47 with a QOC of 2.2/10.

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).