SFL vs SHIP

SFL Corporation Ltd vs Seanergy Maritime Holdings Corp — Valuation Comparison 2026

SFL

Deep Sea Foreign Transportation of Freight
SFL Corporation Ltd
Quality
6.7
out of 10
Value Trap
20
SAFE
Price
$11.04
Last close
Models
11/13
Active
VS

SHIP

Deep Sea Foreign Transportation of Freight
Seanergy Maritime Holdings Corp
Quality
2.1
out of 10
Value Trap
Price
$15.50
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType SFL Fair ValueSFL Upside SHIP Fair ValueSHIP Upside
Bayesian DCF Intrinsic $25.06 +127.0% $3.18 -79.5%
Earnings Power Value Intrinsic $1.32 -88.6% $5.73 -63.7%
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 SFL vs SHIP — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

SFL vs SHIP — Which Stock Is More Undervalued?

SFL scores higher with a 6.7/10 quality rating vs SHIP's 2.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing SFL Corporation Ltd (SFL) and Seanergy Maritime Holdings Corp (SHIP) 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.

SFL currently trades at $11.04 with a QOC of 6.7/10, while SHIP trades at $15.50 with a QOC of 2.1/10.

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).