Federal-Mogul Goetze (India) Limited (FMGOETZE) Fair Value & Analysis
Consumer Cyclical · IN · Market cap ₹25.3B
Fair value as of: Jun 29, 2026
Analysis
Federal-Mogul Goetze (India) Limited (FMGOETZE) currently trades at ₹452.55, while our model-based Fair Value estimate is ₹674.86 — implying the stock looks roughly 49.1% undervalued today. We read business quality at 91/100 (high quality), in the Consumer Cyclical sector. Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: high) — always confirm before acting.
About the company
Federal-Mogul Goetze (India) Limited manufactures, supply, and distributes automotive components for two/three/four-wheeler automobiles in India and internationally. It provides pistons, piston rings, and wet and dry cylinder liners for a range of applications, including bi-wheelers, passenger cars, SUVs, tractors, light and heavy commercial vehicles, locomotive engines, stationary engines, and high output locomotive diesel engines. The company also offers sintered metal products for various engines and automotive applications, such as valve trains, transmission, lubrication pumps, and other engine/structural parts. It exports its products. The company was formerly known as Goetze (India) Limited and changed its name to Federal-Mogul Goetze (India) Limited in 2006. The company was incorporated in 1954 and is based in Gurugram, India. Federal-Mogul Goetze (India) Limited is a subsidiary of Federal Mogul Holding Limited.
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Frequently asked questions
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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.