HSLV vs IAG

Highlander Silver Corp. vs Iamgold Corporation — Valuation Comparison 2026

HSLV

Gold and Silver Ores
Highlander Silver Corp.
Quality
4.4
out of 10
Value Trap
Price
$5.50
Last close
Models
9/13
Active
VS

IAG

Gold and Silver Ores
Iamgold Corporation
Quality
1.9
out of 10
Value Trap
Price
$17.88
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType HSLV Fair ValueHSLV Upside IAG Fair ValueIAG Upside
Bayesian DCF Intrinsic $1.64 -70.2% $4.86 -72.8%
Earnings Power Value Intrinsic $7.60 -55.9%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $0.38 -93.1% $3.74 -79.1%
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 HSLV vs IAG — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

HSLV vs IAG — Which Stock Is More Undervalued?

HSLV scores higher with a 4.4/10 quality rating vs IAG's 1.9/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Highlander Silver Corp. (HSLV) and Iamgold Corporation (IAG) 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.

HSLV currently trades at $5.50 with a QOC of 4.4/10, while IAG trades at $17.88 with a QOC of 1.9/10.

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).