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Maritima de Inversiones SA (MARINSA) Fair Value & Analysis

Industrials · CL · Market cap 274B CLP

Price98.73 CLP
Fair Value163.66 CLP
Upside+65.8%
Quality95/100
Evidence: High Range 122.75 CLP – 204.58 CLP

Fair value as of: Jun 24, 2026

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Analysis

Maritima de Inversiones SA (MARINSA) currently trades at 98.73 CLP, while our model-based Fair Value estimate is 163.66 CLP — implying the stock looks roughly 65.8% undervalued today. We read business quality at 95/100 (high quality), in the Industrials sector. Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: high) — always confirm before acting.

About the company

Maritima de Inversiones SA is a publicly owned investment manager. The firm manages equity and mutual funds. It invests in term deposits, mutual Funds and stocks. The company was founded on March 19, 1982 and is based in Santiago, Chile.

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Frequently asked questions

Is Maritima de Inversiones SA (MARINSA) undervalued?
As of Jun 24, 2026, our model estimates a fair value of 163.66 CLP versus a price of 98.73 CLP — about +66% (undervalued). Model-based estimate, not financial advice.
What is the fair value of MARINSA?
Our 21-model fair value for Maritima de Inversiones SA is 163.66 CLP (as of Jun 24, 2026), built from audited fundamentals. The current price is 98.73 CLP.
What is the quality score of MARINSA?
Maritima de Inversiones SA 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.