BHP vs BMM

BHP Group Limited vs Blue Moon Metals Inc. — Valuation Comparison 2026

BHP

Metal Mining
BHP Group Limited
Quality
2.1
out of 10
Value Trap
Price
$88.91
Last close
Models
13/13
Active
VS

BMM

Metal Mining
Blue Moon Metals Inc.
Quality
1.7
out of 10
Value Trap
Price
$7.69
Last close
Models
7/13
Active

Model-by-Model Comparison

ModelType BHP Fair ValueBHP Upside BMM Fair ValueBMM Upside
Bayesian DCF Intrinsic $27.93 -68.6% $1.80 -76.6%
Earnings Power Value Intrinsic $35.22 -55.9%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $165.01 +85.6% $4.78 -37.8%
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 BHP vs BMM — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

BHP vs BMM — Which Stock Is More Undervalued?

BHP scores higher with a 2.1/10 quality rating vs BMM's 1.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing BHP Group Limited (BHP) and Blue Moon Metals Inc. (BMM) 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.

BHP currently trades at $88.91 with a QOC of 2.1/10, while BMM trades at $7.69 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).