Hoenle AG (HNL) Fair Value & Analysis
Industrials · DE · Market cap €57.5M
Fair value as of: Jun 24, 2026
Analysis
Hoenle AG (HNL) currently trades at €9.12, while our model-based Fair Value estimate is €2.98 — implying the stock looks roughly 67.3% overvalued today. We read business quality at 95/100 (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
Hoenle AG develops, manufactures, and distributes industrial UV technologies and systems in Germany and internationally. It operates through three segments: Adhesive Systems, Curing, and Disinfection. The Adhesives Systems segment offers industrial adhesives, as well as supplies adhesive curing systems used for consumer electronics, automotive, optics and opto-electronics, and curing of adhesives applications. The Curing segment sells equipment for surface drying and curing paints, varnishes, and other coatings for printing, packaging printing, coating, and printing on three dimensional objects industries. The Disinfection segment is involved in activities in the areas of industrial water, surface, and air disinfection, including microbiological testing for various applications comprising disinfection in food industry, special lamps for life science, custom lamps, and microbiologic analyses. It serves aerospace, automotive, consumer electronics, environmental, food, health and medic…
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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.