IAUX vs MTA

i-80 Gold Corp. vs Metalla Royalty & Streaming Ltd — Valuation Comparison 2026

IAUX

Gold and Silver Ores
i-80 Gold Corp.
Quality
5.6
out of 10
Value Trap
6
SAFE
Price
$1.62
Last close
Models
10/13
Active
VS

MTA

Gold and Silver Ores
Metalla Royalty & Streaming Ltd
Quality
1.8
out of 10
Value Trap
6
SAFE
Price
$7.86
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType IAUX Fair ValueIAUX Upside MTA Fair ValueMTA Upside
Bayesian DCF Intrinsic $0.45 -72.1% $1.35 -82.9%
Earnings Power Value Intrinsic $0.35 -94.8%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $0.09 -94.2% $1.35 -82.8%
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 IAUX vs MTA — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

IAUX vs MTA — Which Stock Is More Undervalued?

IAUX scores higher with a 5.6/10 quality rating vs MTA's 1.8/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing i-80 Gold Corp. (IAUX) and Metalla Royalty & Streaming Ltd (MTA) 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.

IAUX currently trades at $1.62 with a QOC of 5.6/10, while MTA trades at $7.86 with a QOC of 1.8/10.

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).