Endurance Technologies Limited (ENDURANCE) Fair Value & Analysis
Consumer Cyclical · IN · Market cap ₹380B
Fair value as of: Jun 29, 2026
Analysis
Endurance Technologies Limited (ENDURANCE) currently trades at ₹2,699, while our model-based Fair Value estimate is ₹1,489 — implying the stock looks roughly 44.9% overvalued today. We read business quality at 97/100 (high quality), in the Consumer Cyclical 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
Endurance Technologies Limited, together with its subsidiaries, manufactures and supplies automotive components for original equipment manufacturers in India and internationally. The company offers aluminium die castings, such as high pressure, low pressure, and gravity die castings; machining components, which comprise engines, gearboxes, and transmission parts; other metallic components, including aluminum alloys, cast iron, and steel; and suspension products. It also designs, develops, and manufactures adjustable and non-adjustable damping force inverted front forks and mono shock absorbers; and provides transmission products comprising clutch assemblies and continuous variable transmission assemblies. In addition, the company offers brake systems that include disc brakes, hydraulic drum brakes, anti-lock braking, combine braking, and tandem master cylinders, as well as disc brake assemblies, and drum brake assemblies, as well as aftermarket sales services. Further, it provides s…
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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.