GLDG vs HYMC

GoldMining Inc. vs Hycroft Mining Holding Corporat — Valuation Comparison 2026

GLDG

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

HYMC

Gold
Hycroft Mining Holding Corporat
Quality
4.8
out of 10
Value Trap
18
SAFE
Price
$33.17
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType GLDG Fair ValueGLDG Upside HYMC Fair ValueHYMC Upside
Bayesian DCF Intrinsic $0.30 -73.3% $10.27 -69.0%
Earnings Power Value Intrinsic $0.16 -85.8% $15.54 -59.2%
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 $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

Access all valuation models for GLDG vs HYMC — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

GLDG vs HYMC — Which Stock Is More Undervalued?

GLDG scores higher with a 5.1/10 quality rating vs HYMC's 4.8/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing GoldMining Inc. (GLDG) and Hycroft Mining Holding Corporat (HYMC) 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.

GLDG currently trades at $1.11 with a QOC of 5.1/10, while HYMC trades at $33.17 with a QOC of 4.8/10.

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).