GFF vs MTEN

Griffon Corporation vs Mingteng International Corporat — Valuation Comparison 2026

GFF

Metal Doors, Sash, Frames, Moldings & Trim
Griffon Corporation
Quality
8.8
out of 10
Value Trap
6
SAFE
Price
$87.98
Last close
Models
11/13
Active
VS

MTEN

Metal Doors, Sash, Frames, Moldings & Trim
Mingteng International Corporat
Quality
2.2
out of 10
Value Trap
Price
$1.16
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType GFF Fair ValueGFF Upside MTEN Fair ValueMTEN Upside
Bayesian DCF Intrinsic $13.29 -84.9% $0.20 -82.4%
Earnings Power Value Intrinsic $8.66 -90.2% $0.27 -74.3%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 GFF vs MTEN — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

GFF vs MTEN — Which Stock Is More Undervalued?

GFF scores higher with a 8.8/10 quality rating vs MTEN's 2.2/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Griffon Corporation (GFF) and Mingteng International Corporat (MTEN) 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.

GFF currently trades at $87.98 with a QOC of 8.8/10, while MTEN trades at $1.16 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).