EP vs EPSN

Empire Petroleum Corporation vs Epsilon Energy Ltd. — Valuation Comparison 2026

EP

Crude Petroleum & Natural Gas
Empire Petroleum Corporation
Quality
5.5
out of 10
Value Trap
44
WARN
Price
$2.54
Last close
Models
10/13
Active
VS

EPSN

Crude Petroleum & Natural Gas
Epsilon Energy Ltd.
Quality
7.2
out of 10
Value Trap
20
SAFE
Price
$5.66
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType EP Fair ValueEP Upside EPSN Fair ValueEPSN Upside
Bayesian DCF Intrinsic $0.25 -90.3% $11.22 +98.2%
Earnings Power Value Intrinsic $2.40 -57.6%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $0.14 -94.7% $4.52 -20.1%
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 EP vs EPSN — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

EP vs EPSN — Which Stock Is More Undervalued?

EPSN scores higher with a 7.2/10 quality rating vs EP's 5.5/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Empire Petroleum Corporation (EP) and Epsilon Energy Ltd. (EPSN) 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.

EP currently trades at $2.54 with a QOC of 5.5/10, while EPSN trades at $5.66 with a QOC of 7.2/10.

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).