Clean Science and Technology Limited (CLEAN) Fair Value & Analysis
Basic Materials · IN · Market cap ₹82.8B
Fair value as of: Jun 29, 2026
Analysis
Clean Science and Technology Limited (CLEAN) currently trades at ₹779.05, while our model-based Fair Value estimate is ₹367.34 — implying the stock looks roughly 52.8% overvalued today. We read business quality at 97/100 (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
Clean Science and Technology Limited, together with its subsidiaries, manufactures fine and specialty chemicals in India, China, the Americas, Europe, and internationally. It operates through Performance Chemicals, FMCG Chemicals, and Pharmaceutical & Agro Intermediates segments. The company offers performance chemicals, including monomethyl ether of hydroquinone, butylated hydroxy anisole, tertiary butyl hydroquinone, hindered amine light stabilisers, and butylated hydroxytoluene, as well as clean antiOX 962, 4-oxo tempo, 4-butoxy tempo, clean light stab 770, 4-hydroxy tempo, l-ascorbyl palmitate, 2,5-di-tertiary butyl hydroquinone, and dimethyl sebacate. It also provides FMCG chemicals, such as 4-methoxy acetophenone, anisole, guaiacol, butylated hydroxy anisole, l-ascorbyl palmitate, tertiary butyl hydroquinone, para di-methoxy benzene, and ortho methoxy toluene. In addition, the company offers pharmaceutical and agro intermediates, which include guaiacol, dicyclohexyl carbodiimi…
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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.