DNLI vs EDIT

Denali Therapeutics Inc. vs Editas Medicine, Inc. — Valuation Comparison 2026

DNLI

Biological Products, (No Diagnostic Substances)
Denali Therapeutics Inc.
Quality
6.9
out of 10
Value Trap
6
SAFE
Price
$21.04
Last close
Models
12/13
Active
VS

EDIT

Biological Products, (No Diagnostic Substances)
Editas Medicine, Inc.
Quality
6.5
out of 10
Value Trap
18
SAFE
Price
$3.46
Last close
Models
9/13
Active

Model-by-Model Comparison

ModelType DNLI Fair ValueDNLI Upside EDIT Fair ValueEDIT Upside
Bayesian DCF Intrinsic $5.29 -74.9% $1.06 -69.4%
Earnings Power Value Intrinsic $9.00 -54.7%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $5.90 -72.0% $2.00 -42.1%
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 DNLI vs EDIT — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

DNLI vs EDIT — Which Stock Is More Undervalued?

DNLI scores higher with a 6.9/10 quality rating vs EDIT's 6.5/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Denali Therapeutics Inc. (DNLI) and Editas Medicine, Inc. (EDIT) 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.

DNLI currently trades at $21.04 with a QOC of 6.9/10, while EDIT trades at $3.46 with a QOC of 6.5/10.

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).