NVO vs SCLX

Novo Nordisk A/S vs Scilex Holding Company — Valuation Comparison 2026

NVO

Drug Manufacturers - General
Novo Nordisk A/S
Quality
10.0
out of 10
Value Trap
12
SAFE
Price
$45.51
Last close
Models
12/13
Active
VS

SCLX

Drug Manufacturers - General
Scilex Holding Company
Quality
4.7
out of 10
Value Trap
44
WARN
Price
$7.21
Last close
Models
5/13
Active

Model-by-Model Comparison

ModelType NVO Fair ValueNVO Upside SCLX Fair ValueSCLX Upside
Bayesian DCF Intrinsic $31.50 -30.8% $33.36 +463.5%
Earnings Power Value Intrinsic $44.05 -3.2%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
RCMH-DCF Intrinsic $26.84 -41.0% $13.06 +81.2%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

NVO vs SCLX — Which Stock Is More Undervalued?

NVO scores higher with a 10.0/10 quality rating vs SCLX's 4.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Novo Nordisk A/S (NVO) and Scilex Holding Company (SCLX) 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.

NVO currently trades at $45.51 with a QOC of 10.0/10, while SCLX trades at $7.21 with a QOC of 4.7/10.

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).