Carborundum Universal Limited (CARBORUNIV) Fair Value & Analysis
Industrials · IN · Market cap ₹217B
Fair value as of: Jun 29, 2026
Analysis
Carborundum Universal Limited (CARBORUNIV) currently trades at ₹1,143, while our model-based Fair Value estimate is ₹173.78 — implying the stock looks roughly 84.8% overvalued today. We read business quality at 94/100 (high quality), in the Industrials 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
Carborundum Universal Limited, together with its subsidiaries, manufactures and sells abrasives, ceramics, and electrominerals in India and internationally. It operates through three segments: Surface Engineering, Technical Ceramics and Super Refractory Solutions, and Electrominerals. The company offers super, bonded and coated abrasives, cutting and grinding, metal working fluids, and power tools; and electro minerals, such as alumina, carbides, zirconia, and grit powders. It also provides industrial ceramics for applications in mining and mineral processing, power and energy systems, mobility, semiconductors and electronics, and aerospace and defence; and manufactures super refractories, including dense bonded, insulating bonded, monolithics, pre-cast prefired components, acid-resistant liners comprising carbon bricks, advanced composites, specialty screeding and coatings, polymer concrete cells, and structural composites, including piping for abrasion resistance; and graphene, ph…
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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.