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Clariant AG (CLN) Fair Value & Analysis

Basic Materials · CH · Market cap CHF 2.6B

PriceCHF 7.43
Fair ValueCHF 10.20
Upside+37.3%
Quality91/100
Evidence: High Range CHF 6.23 – CHF 14.70

Analysis

Clariant AG (CLN) currently trades at CHF 7.43, while our model-based Fair Value estimate is CHF 10.20 — implying the stock looks roughly 37.3% undervalued today. We read business quality at 91/100 (high quality), in the Basic Materials sector. Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: high) — always confirm before acting.

About the company

Clariant AG develops, manufactures, distributes, and sells specialty chemicals in Switzerland, Europe, the Middle East, Africa, the United States, and the Asia Pacific. It operates through Care Chemicals, Catalysts, and Adsorbents & Additives segments. The Care Chemicals segment offers personal and home care, crop solutions, industrial applications, base chemicals, oil services, and mining solutions for various applications in home and personal care, coatings, adhesives, and agriculture, and food. The Catalysis segment provides business services for propylene and ethylene, syngas and fuels, and specialties. The Adsorbents & Additives segment provides coatings, adhesives and polymer solutions for home care, personal care, coatings, adhesives, agriculture, food, electrics, electronics, automotive, oil, building, construction, aviation, and mining. The company was founded in 1886 and is headquartered in Muttenz, Switzerland.

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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.