SB vs SMHI

Safe Bulkers, Inc vs SEACOR Marine Holdings Inc. — Valuation Comparison 2026

SB

Deep Sea Foreign Transportation of Freight
Safe Bulkers, Inc
Quality
2.3
out of 10
Value Trap
Price
$6.25
Last close
Models
13/13
Active
VS

SMHI

Deep Sea Foreign Transportation of Freight
SEACOR Marine Holdings Inc.
Quality
6.1
out of 10
Value Trap
12
SAFE
Price
$7.55
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType SB Fair ValueSB Upside SMHI Fair ValueSMHI Upside
Bayesian DCF Intrinsic $1.57 -74.9%
Earnings Power Value Intrinsic $3.22 -53.4%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $13.17 +89.5% $31.12 +312.2%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $4.06 -35.0% $1.80 -76.1%
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 SB vs SMHI — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

SB vs SMHI — Which Stock Is More Undervalued?

SMHI scores higher with a 6.1/10 quality rating vs SB's 2.3/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Safe Bulkers, Inc (SB) and SEACOR Marine Holdings Inc. (SMHI) 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.

SB currently trades at $6.25 with a QOC of 2.3/10, while SMHI trades at $7.55 with a QOC of 6.1/10.

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).