IEP vs MPC

Icahn Enterprises L.P. - Deposi vs Marathon Petroleum Corporation — Valuation Comparison 2026

IEP

Petroleum Refining
Icahn Enterprises L.P. - Deposi
Quality
5.1
out of 10
Value Trap
12
SAFE
Price
$7.44
Last close
Models
10/13
Active
VS

MPC

Petroleum Refining
Marathon Petroleum Corporation
Quality
7.4
out of 10
Value Trap
24
SAFE
Price
$248.77
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType IEP Fair ValueIEP Upside MPC Fair ValueMPC Upside
Bayesian DCF Intrinsic $6.08 -18.3% $307.16 +23.5%
Earnings Power Value Intrinsic $56.64 -77.2%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $19.16 +157.5% $71.10 -71.4%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 IEP vs MPC — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

IEP vs MPC — Which Stock Is More Undervalued?

MPC scores higher with a 7.4/10 quality rating vs IEP's 5.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Icahn Enterprises L.P. - Deposi (IEP) and Marathon Petroleum Corporation (MPC) 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.

IEP currently trades at $7.44 with a QOC of 5.1/10, while MPC trades at $248.77 with a QOC of 7.4/10.

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).