Excel Industries Limited (EXCELINDUS) Fair Value & Analysis
Basic Materials · IN · Market cap ₹11.3B
Fair value as of: Jun 29, 2026
Analysis
Excel Industries Limited (EXCELINDUS) currently trades at ₹906.50, while our model-based Fair Value estimate is ₹1,023 — implying the stock looks roughly 12.9% undervalued today. We read business quality at 97/100 (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
Excel Industries Limited engages in manufactures and sells chemicals, and environmental and biotech products and services in India and internationally. The company offers specialty chemicals, intermediates and actives catering to various end users like agrochemicals, water treatment, soaps and detergents, lube oil additives, mining chemicals, polymer additives and pharmaceuticals; and active pharmaceutical ingredients (APIs), as well as agrochemical inputs, biocides, soil and water conditioners, phosphorus pentachloride, phosphorus trichloride, and thiophosphoryl chloride. It also provides organic waste management composting, municipal solid waste management, plastic waste management, and construction and demolition waste management services. In addition, the company offers polymer input and veterinary APIs. Excel Industries Limited was founded in 1941 and is headquartered in Mumbai, India.
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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.