COLTENE Holding (CLHLF) Fair Value & Analysis
Healthcare · US · Market cap $365M
Fair value as of: Jun 24, 2026
Analysis
COLTENE Holding (CLHLF) currently trades at $61.16, while our model-based Fair Value estimate is $44.27 — implying the stock looks roughly 27.6% overvalued today. We read business quality at 95/100 (high quality), in the Healthcare 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
COLTENE Holding AG develops, manufactures, and sells disposables, tools, and equipment for dentists and dental laboratories in Europe, the Middle East, Africa, North America, Latin America, and Asia/Oceania. The company offers restoration products, including conventional composites, bulk-fill composites, CAD/CAM solutions, core build-ups, bondings, etching gels, temporary materials, luting, curing lights, characterizations, and parapulpal pins; and endodontics products, such as cold sprays, hand files, shaping and retreatment files, endo motors, rinsing and disinfection system, drying, temporary fillings, sealers, guttapercha points, and posts and drills. It also provides prosthetics comprising bite registration, retractions, A-silicones, C-silicones, and accessories; and treatment auxiliaries consist of dental rolls and dispensers, cotton pellets and dispensers, aspirator tips, dental dams, dental dam clamps and accessories, dental scalers, electrosurgery, and occlusion verificatio…
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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.