NEWP vs ODV

New Pacific Metals Corp. vs Osisko Development Corp. — Valuation Comparison 2026

NEWP

Gold and Silver Ores
New Pacific Metals Corp.
Quality
2.1
out of 10
Value Trap
Price
$4.94
Last close
Models
7/13
Active
VS

ODV

Gold and Silver Ores
Osisko Development Corp.
Quality
5.2
out of 10
Value Trap
24
SAFE
Price
$2.80
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType NEWP Fair ValueNEWP Upside ODV Fair ValueODV Upside
Bayesian DCF Intrinsic $1.29 -73.9% $1.22 -56.4%
Earnings Power Value Intrinsic $1.53 -48.5%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $4.48 -9.3% $3.69 +31.7%
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 NEWP vs ODV — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

NEWP vs ODV — Which Stock Is More Undervalued?

ODV scores higher with a 5.2/10 quality rating vs NEWP's 2.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing New Pacific Metals Corp. (NEWP) and Osisko Development Corp. (ODV) 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.

NEWP currently trades at $4.94 with a QOC of 2.1/10, while ODV trades at $2.80 with a QOC of 5.2/10.

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).