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Beewize S.p.A (BWZ) Fair Value & Analysis

Technology · IT · Market cap €4.0M

Price€0.3700
Fair Value€0.0600
Upside-83.8%
Quality88/100
Evidence: Low Range €0.0500 – €0.0800

Fair value as of: Jun 24, 2026

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Analysis

Beewize S.p.A (BWZ) currently trades at €0.3700, while our model-based Fair Value estimate is €0.0600 — implying the stock looks roughly 83.8% overvalued today. We read business quality at 88/100 (high quality), in the Technology 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: low).

About the company

Beewize S.p.A. operates a network of companies in digital innovation. Its network of companies chooses to systematize strategic and technological skills to simplify processes and improve experiences in the retail sector. Beewize S.p.A. was formerly known as FullSix S.p.A. and changed its name to Beewize S.p.A. in January 2023. The company was founded in 1988 and is based in Milan, Italy.

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Frequently asked questions

Is Beewize S.p.A (BWZ) undervalued?
As of Jun 24, 2026, our model estimates a fair value of €0.0600 versus a price of €0.3700 — about −84% (overvalued). Model-based estimate, not financial advice.
What is the fair value of BWZ?
Our 21-model fair value for Beewize S.p.A is €0.0600 (as of Jun 24, 2026), built from audited fundamentals. The current price is €0.3700.
What is the quality score of BWZ?
Beewize S.p.A has a Quality Score of 88/100, 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.