Schaeffler India Limited (SCHAEFFLER) Fair Value & Analysis
Consumer Cyclical · IN · Market cap ₹631B
Analysis
Schaeffler India Limited (SCHAEFFLER) currently trades at ₹4,300, while our model-based Fair Value estimate is ₹1,548 — implying the stock looks roughly 64.0% 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
Schaeffler India Limited engages in the development, manufacture, and distribution of high-precision roller and ball bearings, and related components in India and internationally. The company operates in two segments, Mobility Components and Related Solutions; and Others. It offers ball, plain, and roller bearings; linear guidance systems; cylindrical, spherical, needle, and tapered roller bearings; mounting and dismounting products; and lubrication, alignment, and condition monitoring products, as well as wheel bearings. The company also provides mechatronic products; release system, timing drive, front end auxiliary drive, steering and suspension, wheel bearing, cooling, dampers, valve train, flywheels; and clutch systems for passenger cars, light commercial vehicles, heavy commercial vehicles, and tractors; assembly machines, testing systems, handling technology, machining centers, digitalization services, robotics, and vision systems; and concentric slave cylinder, clutch master…
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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.