PLG vs PPTA

Platinum Group Metals Ltd. vs Perpetua Resources Corp. — Valuation Comparison 2026

PLG

Gold and Silver Ores
Platinum Group Metals Ltd.
Quality
2.0
out of 10
Value Trap
Price
$1.75
Last close
Models
9/13
Active
VS

PPTA

Gold and Silver Ores
Perpetua Resources Corp.
Quality
5.1
out of 10
Value Trap
18
SAFE
Price
$27.07
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType PLG Fair ValuePLG Upside PPTA Fair ValuePPTA Upside
Bayesian DCF Intrinsic $0.43 -75.3% $11.05 -59.2%
Earnings Power Value Intrinsic $15.33 -47.4%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $0.47 -73.2%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

PLG vs PPTA — Which Stock Is More Undervalued?

PPTA scores higher with a 5.1/10 quality rating vs PLG's 2.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Platinum Group Metals Ltd. (PLG) and Perpetua Resources Corp. (PPTA) 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.

PLG currently trades at $1.75 with a QOC of 2.0/10, while PPTA trades at $27.07 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).