FIX vs MAGH

Comfort Systems USA, Inc. vs Magnitude International Ltd — Valuation Comparison 2026

FIX

Electrical Work
Comfort Systems USA, Inc.
Quality
10.0
out of 10
Value Trap
6
SAFE
Price
$1828.21
Last close
Models
12/13
Active
VS

MAGH

Electrical Work
Magnitude International Ltd
Quality
5.2
out of 10
Value Trap
Price
$6.76
Last close
Models
8/13
Active

Model-by-Model Comparison

ModelType FIX Fair ValueFIX Upside MAGH Fair ValueMAGH Upside
Bayesian DCF Intrinsic $433.72 -76.3%
Earnings Power Value Intrinsic $399.41 -78.2%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $2159.28 +18.1% $0.23 -96.6%
Markov DDM Intrinsic $599.74 -67.2% $0.41 -94.0%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 FIX vs MAGH — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

FIX vs MAGH — Which Stock Is More Undervalued?

FIX scores higher with a 10.0/10 quality rating vs MAGH's 5.2/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Comfort Systems USA, Inc. (FIX) and Magnitude International Ltd (MAGH) 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.

FIX currently trades at $1828.21 with a QOC of 10.0/10, while MAGH trades at $6.76 with a QOC of 5.2/10.

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).