Amadeus FiRe AG (AFGZF) Fair Value & Analysis
Industrials · US · Market cap $469M
Fair value as of: Jun 24, 2026
Analysis
Amadeus FiRe AG (AFGZF) currently trades at $86.34, while our model-based Fair Value estimate is $39.97 — implying the stock looks roughly 53.7% overvalued today. We read business quality at 91/100 (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: high).
About the company
Amadeus FiRe AG provides personnel and training services in Germany. It offers specialized personnel services, such as specialist temporary staffing, permanent placement, interim and project management for professional and management staff in commercial professions and IT fields. The company also provides training and retraining options with a focus on commercial and IT sectors; and training for business clients through open or in-house seminars. In addition, it offers courses and degree programs for private individuals, including professional training in the fields of tax, accounting, and controlling; specialized qualifications in international financial reporting comprising IAS/IFRS and US GAAP; master's degree program in taxation; and publicly funded training programmes, as well as focuses on IT, multimedia, and commercial qualifications under the eduBITES, Masterplan, Comcave College, GFN, Steuer-Fachschule Dr. Endriss, Akademie für Internationale Rechnungslegung, and TaxMaster …
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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.