CIE Automotive India Limited (CIEINDIA) Fair Value & Analysis
Consumer Cyclical · IN · Market cap ₹180B
Fair value as of: Jun 29, 2026
Analysis
CIE Automotive India Limited (CIEINDIA) currently trades at ₹471.30, while our model-based Fair Value estimate is ₹426.45 — implying the stock looks roughly 9.5% 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
CIE Automotive India Limited produces and sells automotive components to original equipment manufacturers and other customers in India, Europe, and internationally. It offers forgings, such as crankshafts, CV joints, knuckles, diff Casw, and spindles; gears, including gears and shafts, e-drive components, flanges/end yokes, and clutch hubs; composites comprising front lid, wind shield-electric-3WH, electric box top tray, and front bumper; and crankcase, pump housing, and brake panel aluminum products. The company also provides iron castings, such as differential, turbine, and axle housings, as well as gear carriers; hard and soft magnets; and stampings, including chassis and structural parts, BIW panels and assemblies, cross car beam, safety assemblies, and fuel tanks. In addition, it offers stub axle as forged and machined, steering shaft, wheel hubs, steering yokes, constant velocity joints, steel metal stamping, components and assemblies; turbo chargers housing; axle and transmis…
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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.