NEOV vs PLUG

NeoVolta Inc. vs Plug Power, Inc. — Valuation Comparison 2026

NEOV

Electrical Equipment & Parts
NeoVolta Inc.
Quality
5.8
out of 10
Value Trap
6
SAFE
Price
$2.00
Last close
Models
10/13
Active
VS

PLUG

Electrical Equipment & Parts
Plug Power, Inc.
Quality
5.9
out of 10
Value Trap
33
LOW
Price
$4.12
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType NEOV Fair ValueNEOV Upside PLUG Fair ValuePLUG Upside
Bayesian DCF Intrinsic $0.68 -66.1% $1.10 -73.3%
Earnings Power Value Intrinsic $0.12 -95.7% $1.38 -56.1%
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 NEOV vs PLUG — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

NEOV vs PLUG — Which Stock Is More Undervalued?

PLUG scores higher with a 5.9/10 quality rating vs NEOV's 5.8/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing NeoVolta Inc. (NEOV) and Plug Power, Inc. (PLUG) 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.

NEOV currently trades at $2.00 with a QOC of 5.8/10, while PLUG trades at $4.12 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).