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Grande Armee Investissement SA (MLGAI) Fair Value & Analysis

Industrials · FR · Market cap €43.1M

Price€137.00
Fair Value€72.42
Upside-47.1%
Quality83/100
Evidence: Medium Range €50.70 – €94.15

Fair value as of: Jun 24, 2026

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Analysis

Grande Armee Investissement SA (MLGAI) currently trades at €137.00, while our model-based Fair Value estimate is €72.42 — implying the stock looks roughly 47.1% overvalued today. We read business quality at 83/100 (high quality), in the Industrials 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

Grande Armee Investissement SA operates in the areas of legal publishing, training, and consulting sectors in France and internationally. The company was incorporated in 1992 and is based in Paris, France.

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Frequently asked questions

Is Grande Armee Investissement SA (MLGAI) undervalued?
As of Jun 24, 2026, our model estimates a fair value of €72.42 versus a price of €137.00 — about −47% (overvalued). Model-based estimate, not financial advice.
What is the fair value of MLGAI?
Our 21-model fair value for Grande Armee Investissement SA is €72.42 (as of Jun 24, 2026), built from audited fundamentals. The current price is €137.00.
What is the quality score of MLGAI?
Grande Armee Investissement SA has a Quality Score of 83/100, 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.