FNV vs FURY

Franco-Nevada Corporation vs Fury Gold Mines Limited — Valuation Comparison 2026

FNV

Gold and Silver Ores
Franco-Nevada Corporation
Quality
2.1
out of 10
Value Trap
Price
$230.70
Last close
Models
13/13
Active
VS

FURY

Gold and Silver Ores
Fury Gold Mines Limited
Quality
4.6
out of 10
Value Trap
12
SAFE
Price
$0.58
Last close
Models
9/13
Active

Model-by-Model Comparison

ModelType FNV Fair ValueFNV Upside FURY Fair ValueFURY Upside
Bayesian DCF Intrinsic $57.17 -75.2% $0.20 -65.7%
Earnings Power Value Intrinsic $94.61 -59.7%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $19.80 -91.4% $0.22 -62.2%
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 FNV vs FURY — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

FNV vs FURY — Which Stock Is More Undervalued?

FURY scores higher with a 4.6/10 quality rating vs FNV's 2.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Franco-Nevada Corporation (FNV) and Fury Gold Mines Limited (FURY) 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.

FNV currently trades at $230.70 with a QOC of 2.1/10, while FURY trades at $0.58 with a QOC of 4.6/10.

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).