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MARPS (MARPS) Fair Value & Analysis

Energy · US · Market cap $8.5M

Price$4.55
Fair Value$3.94
Upside-13.4%
Quality95/100
Evidence: High Range $3.07 – $5.11

Fair value as of: Jun 24, 2026

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Analysis

MARPS (MARPS) currently trades at $4.55, while our model-based Fair Value estimate is $3.94 — implying the stock looks roughly 13.4% overvalued today. We read business quality at 95/100 (high quality), in the Energy sector. Bear case: priced above our estimate, the market already discounts strong expectations. Bull case: above-average quality can justify a premium — the entry price still matters most (evidence: high).

About the company

Marine Petroleum Trust, together with its subsidiary, Marine Petroleum Corporation, operates as a royalty trust primarily in the United States. It has overriding royalty interest in oil and natural gas leases in the Central and Western areas of the Gulf of America off the coasts of Louisiana and Texas. The company was founded in 1956 and is based in Dallas, Texas.

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Frequently asked questions

Is MARPS (MARPS) undervalued?
As of Jun 24, 2026, our model estimates a fair value of $3.94 versus a price of $4.55 — about −13% (overvalued). Model-based estimate, not financial advice.
What is the fair value of MARPS?
Our 21-model fair value for MARPS is $3.94 (as of Jun 24, 2026), built from audited fundamentals. The current price is $4.55.
What is the quality score of MARPS?
MARPS has a Quality Score of 95/100, measuring profitability, growth and balance-sheet strength from non-valuation factors.

How we calculate Fair Value

Each company is valued through a stack of independent intrinsic-value models (DCF variants, residual-income, multiples and more), blended into one family-balanced consensus and weighted by how much trustworthy data backs it. A separate quality layer scores the fundamentals. Every input is real reported data — nothing guessed.

Educational research only · not financial advice · no buy/sell recommendation. Model-based estimates are not certainties; their reliability depends on data quality and assumptions.