AGI vs AUGO

Alamos Gold Inc. vs Aura Minerals Inc. — Valuation Comparison 2026

AGI

Gold
Alamos Gold Inc.
Quality
2.3
out of 10
Value Trap
Price
$39.52
Last close
Models
13/13
Active
VS

AUGO

Gold
Aura Minerals Inc.
Quality
1.7
out of 10
Value Trap
Price
$75.88
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType AGI Fair ValueAGI Upside AUGO Fair ValueAUGO Upside
Bayesian DCF Intrinsic $9.75 -75.3% $22.39 -70.5%
Earnings Power Value Intrinsic $19.07 -54.6% $36.46 -59.6%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
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 AGI vs AUGO — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

AGI vs AUGO — Which Stock Is More Undervalued?

AGI scores higher with a 2.3/10 quality rating vs AUGO's 1.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Alamos Gold Inc. (AGI) and Aura Minerals Inc. (AUGO) 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.

AGI currently trades at $39.52 with a QOC of 2.3/10, while AUGO trades at $75.88 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).