Gabriel India Limited (GABRIEL) Fair Value & Analysis
Consumer Cyclical · IN · Market cap ₹217B
Fair value as of: Jun 29, 2026
Analysis
Gabriel India Limited (GABRIEL) currently trades at ₹1,229, while our model-based Fair Value estimate is ₹299.60 — implying the stock looks roughly 75.6% overvalued today. We read business quality at 95/100 (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
Gabriel India Limited manufactures and sells of ride control products to the automotive industry in India, the Netherlands, and internationally. The company provides canister shock absorbers, telescopic front forks, inverted front forks, floating piston shock absorbers, mono-tube and twin-tube shock absorbers, semi-active front forks, semi-active shock absorbers, semi-active suspension, mono shox, rear shock absorbers, strut assemblies, FSD suspension, and axle, cabin, and seat dampers. It also offers double-acting hydraulic shock absorbers for conventional coaches, shock absorbers for EMU/MEMU/DMU coaches, dampers for diesel locomotives, dampers for rajdhani and shatabdi coaches, dampers for ICF Train 18 - Vande Bharat coaches, and dampers for electric locomotives. In addition, the company provides macpherson struts, gas springs, brake pads, drive shafts, suspension parts, suspension and strut bush kits, OC springs, coolants, brake fluids, front fork components, oil seals, front fo…
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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.