NICM vs RIO

Nicola Mining Inc. vs Rio Tinto Plc — Valuation Comparison 2026

NICM

Metal Mining
Nicola Mining Inc.
Quality
1.7
out of 10
Value Trap
Price
$6.65
Last close
Models
6/13
Active
VS

RIO

Metal Mining
Rio Tinto Plc
Quality
2.2
out of 10
Value Trap
Price
$106.39
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType NICM Fair ValueNICM Upside RIO Fair ValueRIO Upside
Bayesian DCF Intrinsic $1.62 -75.6% $28.86 -72.9%
Earnings Power Value Intrinsic $47.89 -54.6%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $0.35 -94.2% $166.38 +56.4%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
RCMH-DCF Intrinsic $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

Access all valuation models for NICM vs RIO — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

NICM vs RIO — Which Stock Is More Undervalued?

RIO scores higher with a 2.2/10 quality rating vs NICM's 1.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Nicola Mining Inc. (NICM) and Rio Tinto Plc (RIO) 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.

NICM currently trades at $6.65 with a QOC of 1.7/10, while RIO trades at $106.39 with a QOC of 2.2/10.

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).