OGI vs OMER

Organigram Global Inc. vs Omeros Corporation — Valuation Comparison 2026

OGI

Pharmaceutical Preparations
Organigram Global Inc.
Quality
5.6
out of 10
Value Trap
Price
$1.14
Last close
Models
11/13
Active
VS

OMER

Pharmaceutical Preparations
Omeros Corporation
Quality
3.3
out of 10
Value Trap
34
LOW
Price
$11.04
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType OGI Fair ValueOGI Upside OMER Fair ValueOMER Upside
Bayesian DCF Intrinsic $0.69 -39.3% $2.82 -74.5%
Earnings Power Value Intrinsic $2.38 +67.7%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $1.08 -5.0% $3.03 -72.5%
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 OGI vs OMER — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

OGI vs OMER — Which Stock Is More Undervalued?

OGI scores higher with a 5.6/10 quality rating vs OMER's 3.3/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Organigram Global Inc. (OGI) and Omeros Corporation (OMER) 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.

OGI currently trades at $1.14 with a QOC of 5.6/10, while OMER trades at $11.04 with a QOC of 3.3/10.

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).