MSGY vs MTRX

Masonglory Limited vs Matrix Service Company — Valuation Comparison 2026

MSGY

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

MTRX

Engineering & Construction
Matrix Service Company
Quality
7.3
out of 10
Value Trap
18
SAFE
Price
$13.23
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType MSGY Fair ValueMSGY Upside MTRX Fair ValueMTRX Upside
Bayesian DCF Intrinsic $0.12 -73.6% $6.87 -48.1%
Earnings Power Value Intrinsic $0.13 -72.0% $12.20 -7.8%
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 MSGY vs MTRX — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

MSGY vs MTRX — Which Stock Is More Undervalued?

MTRX scores higher with a 7.3/10 quality rating vs MSGY's 2.2/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Masonglory Limited (MSGY) and Matrix Service Company (MTRX) 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.

MSGY currently trades at $0.47 with a QOC of 2.2/10, while MTRX trades at $13.23 with a QOC of 7.3/10.

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).