CVE vs E

Cenovus Energy Inc vs ENI S.p.A. — Valuation Comparison 2026

CVE

Crude Petroleum & Natural Gas
Cenovus Energy Inc
Quality
1.9
out of 10
Value Trap
Price
$27.57
Last close
Models
13/13
Active
VS

E

Crude Petroleum & Natural Gas
ENI S.p.A.
Quality
1.7
out of 10
Value Trap
Price
$52.16
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType CVE Fair ValueCVE Upside E Fair ValueE Upside
Bayesian DCF Intrinsic $10.29 -62.7% $18.48 -64.6%
Earnings Power Value Intrinsic $10.75 -59.1% $18.94 -65.0%
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 $•••.•• ••.•% $•••.•• ••.•%
RCMH-DCF Intrinsic $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

CVE vs E — Which Stock Is More Undervalued?

CVE scores higher with a 1.9/10 quality rating vs E's 1.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Cenovus Energy Inc (CVE) and ENI S.p.A. (E) 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.

CVE currently trades at $27.57 with a QOC of 1.9/10, while E trades at $52.16 with a QOC of 1.7/10.

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).