Is CISS or CTRM more undervalued in 2026?

CirclFi compares C3is Inc. (CISS) and Castor Maritime Inc. (CTRM) using 13 institutional-grade valuation models. CISS has a Quality of Company score of 6.9/10 while CTRM scores 7.6/10.

Which is a better buy — CISS or CTRM?

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 CISS and CTRM valuations compare?

CISS at $2.28 and CTRM at $1.90 are compared across Bayesian DCF, EPV, EROIC Spread, First Chicago, Markov DDM, and 8 additional models.

CISS vs CTRM Valuation Comparison 2026 — Intrinsic Value Analysis

CISS

Deep Sea Foreign Transportation of Freight

C3is Inc.

Quality

6.9

out of 10

Value Trap

LOW

Price

$2.28

Last close

Models

2/13

Active

CTRM

Deep Sea Foreign Transportation of Freight

Castor Maritime Inc.

Quality

7.6

out of 10

Value Trap

SAFE

Price

$1.90

Last close

Models

3/13

Active

Model-by-Model Comparison

Model	Type	CISS Fair Value	CISS Upside	CTRM Fair Value	CTRM Upside
Bayesian DCF	Intrinsic	$11.17	+389.9%	—	—
Sentiment SOTP	Hybrid	—	—	$11.19	+488.8%
FTNN Topology	Relative	$•••.••	••.•%	$•••.••	••.•%
RCMH-DCF	Intrinsic	$10.37	+354.9%	$9.46	+397.8%

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CISS vs CTRM — Which Stock Is More Undervalued?

CTRM scores higher with a 7.6/10 quality rating vs CISS's 6.9/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing C3is Inc. (CISS) and Castor Maritime Inc. (CTRM) 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.

CISS currently trades at $2.28 with a QOC of 6.9/10, while CTRM trades at $1.90 with a QOC of 7.6/10.

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).