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CrowdFundMe S.p.A (CFM) Fair Value & Analysis

Financial Services · IT · Market cap €2.1M

Price€1.10
Fair Value€0.5200
Upside-52.7%
Quality94/100
Evidence: Low Range €0.3900 – €0.7800

Fair value as of: Jun 26, 2026

Analysis

CrowdFundMe S.p.A (CFM) currently trades at €1.10, while our model-based Fair Value estimate is €0.5200 — implying the stock looks roughly 52.7% overvalued today. We read business quality at 94/100 (high quality), in the Financial Services 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

CrowdFundMe S.p.A. operates a crowdfunding portal for companies and investor to raise funding. The company's portal offers investing in unlisted companies, selected among those that have the greatest growth potential. CrowdFundMe was incorporated in 2013 and is based in Milan, Italy.

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Frequently asked questions

Is CrowdFundMe S.p.A (CFM) undervalued?
As of Jun 26, 2026, our model estimates a fair value of €0.5200 versus a price of €1.10 — about −53% (overvalued). Model-based estimate, not financial advice.
What is the fair value of CFM?
Our 21-model fair value for CrowdFundMe S.p.A is €0.5200 (as of Jun 26, 2026), built from audited fundamentals. The current price is €1.10.
What is the quality score of CFM?
CrowdFundMe S.p.A has a Quality Score of 94/100, 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.