GTE vs HESM

Gran Tierra Energy Inc. vs Hess Midstream LP — Valuation Comparison 2026

GTE

Crude Petroleum & Natural Gas
Gran Tierra Energy Inc.
Quality
6.0
out of 10
Value Trap
18
SAFE
Price
$7.77
Last close
Models
9/13
Active
VS

HESM

Crude Petroleum & Natural Gas
Hess Midstream LP
Quality
9.5
out of 10
Value Trap
12
SAFE
Price
$37.50
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType GTE Fair ValueGTE Upside HESM Fair ValueHESM Upside
Bayesian DCF Intrinsic $63.07 +68.2%
Earnings Power Value Intrinsic $1.27 -86.2% $12.95 -65.5%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $11.81 +52.0%
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 GTE vs HESM — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

GTE vs HESM — Which Stock Is More Undervalued?

HESM scores higher with a 9.5/10 quality rating vs GTE's 6.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Gran Tierra Energy Inc. (GTE) and Hess Midstream LP (HESM) 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.

GTE currently trades at $7.77 with a QOC of 6.0/10, while HESM trades at $37.50 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).