TOPS vs TRMD

TOP Ships, Inc. vs TORM plc — Valuation Comparison 2026

TOPS

Deep Sea Foreign Transportation of Freight
TOP Ships, Inc.
Quality
1.5
out of 10
Value Trap
15
SAFE
Price
$0.90
Last close
Models
3/13
Active
VS

TRMD

Deep Sea Foreign Transportation of Freight
TORM plc
Quality
1.9
out of 10
Value Trap
Price
$27.24
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType TOPS Fair ValueTOPS Upside TRMD Fair ValueTRMD Upside
Bayesian DCF Intrinsic $0.38 -57.7% $9.73 -64.3%
Earnings Power Value Intrinsic $4.23 +96.7% $13.92 -55.3%
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 TOPS vs TRMD — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

TOPS vs TRMD — Which Stock Is More Undervalued?

TRMD scores higher with a 1.9/10 quality rating vs TOPS's 1.5/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing TOP Ships, Inc. (TOPS) and TORM plc (TRMD) 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.

TOPS currently trades at $0.90 with a QOC of 1.5/10, while TRMD trades at $27.24 with a QOC of 1.9/10.

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).