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MLGRC (MLGRC) Fair Value & Analysis

Consumer Defensive · FR · Market cap €58.8M

Price€28.00
Fair Value€4.11
Upside-85.3%
Quality87/100
Evidence: Medium Range €3.08 – €5.13

Fair value as of: Jun 26, 2026

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Analysis

MLGRC (MLGRC) currently trades at €28.00, while our model-based Fair Value estimate is €4.11 — implying the stock looks roughly 85.3% overvalued today. We read business quality at 87/100 (high quality), in the Consumer Defensive 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: medium).

About the company

Groupe Carnivor Société Anonyme processes, packages, and distributes meat products in France. The company offers beef, calf, lamb, pork, poultry, sausage, and offal; and fresh produce, including dairy, cold cuts, fresh pasta, and prepared meals. It also provides deli meats, cheeses, grocery items, condiments, fruits, and vegetables. The company was incorporated in 1994 and is based in Toulon, France.

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Frequently asked questions

Is MLGRC (MLGRC) undervalued?
As of Jun 26, 2026, our model estimates a fair value of €4.11 versus a price of €28.00 — about −85% (overvalued). Model-based estimate, not financial advice.
What is the fair value of MLGRC?
Our 21-model fair value for MLGRC is €4.11 (as of Jun 26, 2026), built from audited fundamentals. The current price is €28.00.
What is the quality score of MLGRC?
MLGRC has a Quality Score of 87/100, 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.