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Magis S.p.A (MGS) Fair Value & Analysis

Basic Materials · IT · Market cap €56.1M

Price€9.85
Fair Value€7.06
Upside-28.3%
Quality95/100
Evidence: High Range €6.01 – €7.94

Fair value as of: Jun 24, 2026

Analysis

Magis S.p.A (MGS) currently trades at €9.85, while our model-based Fair Value estimate is €7.06 — implying the stock looks roughly 28.3% overvalued today. We read business quality at 95/100 (high quality), in the Basic Materials 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

Magis S.p.A. engages in the manufacture and sale of adhesive tapes and closure systems for disposable diapers and products for adult incontinence. The company offers neutral and printed polypropylene adhesive tapes, double-sided adhesive papers, and mechanical closures for diapers under the Frontal Tape and Side Tape brand names. Magis S.p.A. was founded in 1983 and is headquartered in Cerreto Guidi, Italy.

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Frequently asked questions

Is Magis S.p.A (MGS) undervalued?
As of Jun 24, 2026, our model estimates a fair value of €7.06 versus a price of €9.85 — about −28% (overvalued). Model-based estimate, not financial advice.
What is the fair value of MGS?
Our 21-model fair value for Magis S.p.A is €7.06 (as of Jun 24, 2026), built from audited fundamentals. The current price is €9.85.
What is the quality score of MGS?
Magis S.p.A 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.