CLDX vs CLLS

Celldex Therapeutics, Inc. vs Cellectis S.A. — Valuation Comparison 2026

CLDX

Biotechnology
Celldex Therapeutics, Inc.
Quality
5.6
out of 10
Value Trap
47
WARN
Price
$31.70
Last close
Models
12/13
Active
VS

CLLS

Biotechnology
Cellectis S.A.
Quality
2.5
out of 10
Value Trap
6
SAFE
Price
$3.63
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType CLDX Fair ValueCLDX Upside CLLS Fair ValueCLLS Upside
Bayesian DCF Intrinsic $9.79 -69.1% $0.96 -73.5%
Earnings Power Value Intrinsic $1.05 -96.8% $1.52 -61.5%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

CLDX vs CLLS — Which Stock Is More Undervalued?

CLDX scores higher with a 5.6/10 quality rating vs CLLS's 2.5/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Celldex Therapeutics, Inc. (CLDX) and Cellectis S.A. (CLLS) 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.

CLDX currently trades at $31.70 with a QOC of 5.6/10, while CLLS trades at $3.63 with a QOC of 2.5/10.

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).