Is NCT or PSHG more undervalued in 2026?

CirclFi compares Intercont (Cayman) Limited (NCT) and Performance Shipping Inc. (PSHG) using 13 institutional-grade valuation models. NCT has a Quality of Company score of 8.0/10 while PSHG scores 8.2/10.

Which is a better buy — NCT or PSHG?

Compare intrinsic values from 13 independent models against current market prices. CirclFi also calculates Value Trap scores. This is not financial advice — conduct your own analysis.

How do NCT and PSHG valuations compare?

NCT at $2.79 and PSHG at $1.73 are compared across Bayesian DCF, EPV, EROIC Spread, First Chicago, Markov DDM, and 8 additional models.

NCT vs PSHG Valuation Comparison 2026 — Intrinsic Value Analysis

NCT

Deep Sea Foreign Transportation of Freight

Intercont (Cayman) Limited

Quality

8.0

out of 10

Value Trap

—

Price

$2.79

Last close

Models

1/13

Active

PSHG

Deep Sea Foreign Transportation of Freight

Performance Shipping Inc.

Quality

8.2

out of 10

Value Trap

SAFE

Price

$1.73

Last close

Models

1/13

Active

Model-by-Model Comparison

Model	Type	NCT Fair Value	NCT Upside	PSHG Fair Value	PSHG Upside
Sentiment SOTP	Hybrid	$8.47	+203.4%	—	—
FTNN Topology	Relative	—	—	$6.37	+268.2%

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NCT vs PSHG — Which Stock Is More Undervalued?

PSHG scores higher with a 8.2/10 quality rating vs NCT's 8.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Intercont (Cayman) Limited (NCT) and Performance Shipping Inc. (PSHG) 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.

NCT currently trades at $2.79 with a QOC of 8.0/10, while PSHG trades at $1.73 with a QOC of 8.2/10.

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).