FSM vs GLDG

Fortuna Mining Corp. vs GoldMining Inc. — Valuation Comparison 2026

FSM

Gold
Fortuna Mining Corp.
Quality
8.8
out of 10
Value Trap
24
SAFE
Price
$9.81
Last close
Models
13/13
Active
VS

GLDG

Gold
GoldMining Inc.
Quality
5.1
out of 10
Value Trap
6
SAFE
Price
$1.11
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType FSM Fair ValueFSM Upside GLDG Fair ValueGLDG Upside
Bayesian DCF Intrinsic $15.78 +60.9% $0.30 -73.3%
Earnings Power Value Intrinsic $10.93 +11.4% $0.16 -85.8%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 FSM vs GLDG — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

FSM vs GLDG — Which Stock Is More Undervalued?

FSM scores higher with a 8.8/10 quality rating vs GLDG's 5.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Fortuna Mining Corp. (FSM) and GoldMining Inc. (GLDG) 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.

FSM currently trades at $9.81 with a QOC of 8.8/10, while GLDG trades at $1.11 with a QOC of 5.1/10.

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).