NERV vs NKTR

Minerva Neurosciences, Inc vs Nektar Therapeutics — Valuation Comparison 2026

NERV

Pharmaceutical Preparations
Minerva Neurosciences, Inc
Quality
3.4
out of 10
Value Trap
24
SAFE
Price
$4.91
Last close
Models
6/13
Active
VS

NKTR

Pharmaceutical Preparations
Nektar Therapeutics
Quality
5.9
out of 10
Value Trap
24
SAFE
Price
$64.88
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType NERV Fair ValueNERV Upside NKTR Fair ValueNKTR Upside
Bayesian DCF Intrinsic $1.70 -65.3% $20.10 -69.0%
Earnings Power Value Intrinsic $5.62 -93.5%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $11.74 +139.1% $69.48 +7.1%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

NERV vs NKTR — Which Stock Is More Undervalued?

NKTR scores higher with a 5.9/10 quality rating vs NERV's 3.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Minerva Neurosciences, Inc (NERV) and Nektar Therapeutics (NKTR) 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.

NERV currently trades at $4.91 with a QOC of 3.4/10, while NKTR trades at $64.88 with a QOC of 5.9/10.

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).