Aegis Logistics Limited (AEGISLOG) Fair Value & Analysis
Energy · IN · Market cap ₹399B
Fair value as of: Jun 29, 2026
Analysis
Aegis Logistics Limited (AEGISLOG) currently trades at ₹1,141, while our model-based Fair Value estimate is ₹310.51 — implying the stock looks roughly 72.8% overvalued today. We read business quality at 82/100 (high quality), in the Energy 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: medium).
About the company
Aegis Logistics Limited, together with its subsidiaries, operates as an oil, gas, and chemical logistics company primarily in India. The company operates through Liquid Terminal Division and Gas Terminal Division segments. It owns and operates a network of shore based tank farm installations for the handling of bulk liquids, including hazardous chemicals, petroleum products, and petrochemicals for petroleum, oil, petrochemical, chemical, and vegetable oil industries. The company also offers supply chain management services, including product planning, sourcing, shipping, receipt, storage, and dispatch; product handling services; storage services for other related gases, such as Butene-1, Butadiene, Propylene, VCM, etc.; and energy solution to industries for their various applications, as well as supplies LPG, propane, and butane. In addition, it provides fuel transportation services; and LPG installation and interfuel conversion services for home, hotels, and industries, as well as …
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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.