LNKS vs NEOV

Linkers Industries Limited vs NeoVolta Inc. — Valuation Comparison 2026

LNKS

Electrical Equipment & Parts
Linkers Industries Limited
Quality
4.9
out of 10
Value Trap
8
SAFE
Price
$1.78
Last close
Models
11/13
Active
VS

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

Model-by-Model Comparison

ModelType LNKS Fair ValueLNKS Upside NEOV Fair ValueNEOV Upside
Bayesian DCF Intrinsic $2.65 +49.0% $0.68 -66.1%
Earnings Power Value Intrinsic $0.64 -59.5% $0.12 -95.7%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
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 LNKS vs NEOV — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

LNKS vs NEOV — Which Stock Is More Undervalued?

NEOV scores higher with a 5.8/10 quality rating vs LNKS's 4.9/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Linkers Industries Limited (LNKS) and NeoVolta Inc. (NEOV) 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.

LNKS currently trades at $1.78 with a QOC of 4.9/10, while NEOV trades at $2.00 with a QOC of 5.8/10.

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).