Camlin Fine Sciences Limited (CAMLINFINE) Fair Value & Analysis
Basic Materials · IN · Market cap ₹26.1B
Fair value as of: Jun 29, 2026
Analysis
Camlin Fine Sciences Limited (CAMLINFINE) currently trades at ₹136.37, while our model-based Fair Value estimate is ₹27.43 — implying the stock looks roughly 79.9% overvalued today. We read business quality at 94/100 (high quality), in the Basic Materials 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
Camlin Fine Sciences Limited, together with its subsidiaries, research, develops, manufactures, and markets specialty chemicals, ingredients, and additive blend products in India and internationally. The company offers shelf-life solutions, which include basic ingredients, such as tert-butylhydroquinone (TBHQ), Butylated Hydroxyanisole (BHA), Butylated Hydroxytoluene (BHT), and Ascorbyl Palmitate (ASP); aroma ingredients, including vanillin and ethyl vanillin, natural vanillin, and some of its combinations; performance chemicals that include Hydroquinone (HQ), Catechol (CAT), Monomethyl Ether of HQ (MEHQ), tert butylcatechol (TBC), Guaiacol, and Veratrole; and health and wellness products comprising algae based products, such as Omega 3 fatty acids. It sells its prodycrts under the Xtendra, Nasure, Ezential, adorr, Dinamic, and Biosus brands. The company's products are used in food and beverage, pet food and rendering, animal nutrition, petrochemical, nutrition and health, biofuel, …
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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.