EGY vs EOG

VAALCO Energy, Inc. vs EOG Resources, Inc. — Valuation Comparison 2026

EGY

Crude Petroleum & Natural Gas
VAALCO Energy, Inc.
Quality
6.6
out of 10
Value Trap
30
LOW
Price
$5.22
Last close
Models
12/13
Active
VS

EOG

Crude Petroleum & Natural Gas
EOG Resources, Inc.
Quality
9.5
out of 10
Value Trap
12
SAFE
Price
$133.38
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType EGY Fair ValueEGY Upside EOG Fair ValueEOG Upside
Bayesian DCF Intrinsic $290.98 +118.2%
Earnings Power Value Intrinsic $0.74 -86.8% $89.24 -33.1%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $22.05 +322.3% $253.69 +90.2%
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 EGY vs EOG — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

EGY vs EOG — Which Stock Is More Undervalued?

EOG scores higher with a 9.5/10 quality rating vs EGY's 6.6/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing VAALCO Energy, Inc. (EGY) and EOG Resources, Inc. (EOG) 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.

EGY currently trades at $5.22 with a QOC of 6.6/10, while EOG trades at $133.38 with a QOC of 9.5/10.

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).