RGLD vs TRX

Royal Gold, Inc. vs TRX Gold Corporation — Valuation Comparison 2026

RGLD

Gold
Royal Gold, Inc.
Quality
10.0
out of 10
Value Trap
18
SAFE
Price
$222.68
Last close
Models
13/13
Active
VS

TRX

Gold
TRX Gold Corporation
Quality
1.7
out of 10
Value Trap
Price
$1.10
Last close
Models
9/13
Active

Model-by-Model Comparison

ModelType RGLD Fair ValueRGLD Upside TRX Fair ValueTRX Upside
Bayesian DCF Intrinsic $116.00 -47.9% $0.29 -73.5%
Earnings Power Value Intrinsic $70.50 -68.3%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $144.07 -35.3% $0.93 -14.5%
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 RGLD vs TRX — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

RGLD vs TRX — Which Stock Is More Undervalued?

RGLD scores higher with a 10.0/10 quality rating vs TRX's 1.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Royal Gold, Inc. (RGLD) and TRX Gold Corporation (TRX) 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.

RGLD currently trades at $222.68 with a QOC of 10.0/10, while TRX trades at $1.10 with a QOC of 1.7/10.

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).