Agarwal Industrial Corporation (AGARIND) Fair Value & Analysis
Basic Materials · IN · Market cap ₹8.1B
Fair value as of: Jun 29, 2026
Analysis
Agarwal Industrial Corporation (AGARIND) currently trades at ₹546.20, while our model-based Fair Value estimate is ₹495.14 — implying the stock looks roughly 9.3% 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
Agarwal Industrial Corporation Limited manufactures and trades in petrochemicals in India and internationally. It operates through five segments: Ancillary Infra - Bitumen & Allied Products, Petroleum Vessels Operating and Chartering, Petroleum Products, Logistics, and Windmill. The company provides various bituminous products, including viscosity grade bitumen and industrial grade bitumen; paving grade bitumen and crumb rubber modified bitumen; polymer modified bitumen, such as ethylene vinyl acetate, ethylene butyl acrylate, ethylene ter polymer, styrene-butadiene styrene, and styrene isoprene styrene; byproducts waterproof materials comprising bitubond, bituplast, bituminous black, bitukote, bituprimer, cable compound, and bitufelt; bitumen insulation materials; and bitumen emulsions. It is involved in the transportation of bulk bitumen and liquefied petroleum gas; rubber processing oil; and generation of power through windmills, as well as imports and stores bitumen. The company…
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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.