MTRX vs OFAL

Matrix Service Company vs OFA Group — Valuation Comparison 2026

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
VS

OFAL

Engineering & Construction
OFA Group
Quality
2.0
out of 10
Value Trap
Price
$0.25
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType MTRX Fair ValueMTRX Upside OFAL Fair ValueOFAL Upside
Bayesian DCF Intrinsic $6.87 -48.1% $0.07 -73.5%
Earnings Power Value Intrinsic $12.20 -7.8% $0.02 -97.7%
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 MTRX vs OFAL — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

MTRX vs OFAL — Which Stock Is More Undervalued?

MTRX scores higher with a 7.3/10 quality rating vs OFAL's 2.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Matrix Service Company (MTRX) and OFA Group (OFAL) 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.

MTRX currently trades at $13.23 with a QOC of 7.3/10, while OFAL trades at $0.25 with a QOC of 2.0/10.

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).