HBM vs LAC

Hudbay Minerals Inc. vs Lithium Americas Corp. — Valuation Comparison 2026

HBM

Metal Mining
Hudbay Minerals Inc.
Quality
2.2
out of 10
Value Trap
6
SAFE
Price
$29.16
Last close
Models
13/13
Active
VS

LAC

Metal Mining
Lithium Americas Corp.
Quality
5.0
out of 10
Value Trap
6
SAFE
Price
$5.21
Last close
Models
7/13
Active

Model-by-Model Comparison

ModelType HBM Fair ValueHBM Upside LAC Fair ValueLAC Upside
Bayesian DCF Intrinsic $10.06 -65.5% $1.52 -70.8%
Earnings Power Value Intrinsic $10.60 -55.8%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $4.45 -84.7% $1.14 -78.2%
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 HBM vs LAC — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

HBM vs LAC — Which Stock Is More Undervalued?

LAC scores higher with a 5.0/10 quality rating vs HBM's 2.2/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Hudbay Minerals Inc. (HBM) and Lithium Americas Corp. (LAC) 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.

HBM currently trades at $29.16 with a QOC of 2.2/10, while LAC trades at $5.21 with a QOC of 5.0/10.

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).