NPWR vs WWD

NET Power Inc. vs Woodward, Inc. — Valuation Comparison 2026

NPWR

Electrical Industrial Apparatus
NET Power Inc.
Quality
4.5
out of 10
Value Trap
44
WARN
Price
$2.01
Last close
Models
8/13
Active
VS

WWD

Electrical Industrial Apparatus
Woodward, Inc.
Quality
8.0
out of 10
Value Trap
18
SAFE
Price
$350.03
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType NPWR Fair ValueNPWR Upside WWD Fair ValueWWD Upside
Bayesian DCF Intrinsic $82.10 -76.5%
Earnings Power Value Intrinsic $69.72 -80.1%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $0.13 -93.2% $182.41 -47.9%
Dynamic NAV Asset-Based $0.38 -81.2% $9.12 -97.4%
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 NPWR vs WWD — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

NPWR vs WWD — Which Stock Is More Undervalued?

WWD scores higher with a 8.0/10 quality rating vs NPWR's 4.5/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing NET Power Inc. (NPWR) and Woodward, Inc. (WWD) 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.

NPWR currently trades at $2.01 with a QOC of 4.5/10, while WWD trades at $350.03 with a QOC of 8.0/10.

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).