CBUS vs CF

Cibus, Inc. vs CF Industries Holdings, Inc. — Valuation Comparison 2026

CBUS

Agricultural Chemicals
Cibus, Inc.
Quality
5.7
out of 10
Value Trap
33
LOW
Price
$1.44
Last close
Models
8/13
Active
VS

CF

Agricultural Chemicals
CF Industries Holdings, Inc.
Quality
10.0
out of 10
Value Trap
12
SAFE
Price
$112.35
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType CBUS Fair ValueCBUS Upside CF Fair ValueCF Upside
Bayesian DCF Intrinsic $0.36 -75.0% $275.40 +145.1%
Earnings Power Value Intrinsic $113.05 +0.6%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $2.01 +39.7% $104.50 -7.0%
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 CBUS vs CF — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

CBUS vs CF — Which Stock Is More Undervalued?

CF scores higher with a 10.0/10 quality rating vs CBUS's 5.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Cibus, Inc. (CBUS) and CF Industries Holdings, Inc. (CF) 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.

CBUS currently trades at $1.44 with a QOC of 5.7/10, while CF trades at $112.35 with a QOC of 10.0/10.

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).