KEN vs NNE

Kenon Holdings Ltd. vs Nano Nuclear Energy Inc. — Valuation Comparison 2026

KEN

Electric Services
Kenon Holdings Ltd.
Quality
1.8
out of 10
Value Trap
Price
$90.77
Last close
Models
13/13
Active
VS

NNE

Electric Services
Nano Nuclear Energy Inc.
Quality
5.4
out of 10
Value Trap
6
SAFE
Price
$28.88
Last close
Models
9/13
Active

Model-by-Model Comparison

ModelType KEN Fair ValueKEN Upside NNE Fair ValueNNE Upside
Bayesian DCF Intrinsic $24.96 -72.5% $8.51 -70.5%
Earnings Power Value Intrinsic $46.33 -43.3%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $53.12 -41.5% $1.65 -93.4%
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 KEN vs NNE — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

KEN vs NNE — Which Stock Is More Undervalued?

NNE scores higher with a 5.4/10 quality rating vs KEN's 1.8/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Kenon Holdings Ltd. (KEN) and Nano Nuclear Energy Inc. (NNE) 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.

KEN currently trades at $90.77 with a QOC of 1.8/10, while NNE trades at $28.88 with a QOC of 5.4/10.

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).