Gestamp Automoción, S.A (GMPUF) Fair Value & Analysis
Consumer Cyclical · US · Market cap $2.1B
Fair value as of: Jun 25, 2026
Analysis
Gestamp Automoción, S.A (GMPUF) currently trades at $3.73, while our model-based Fair Value estimate is $6.69 — implying the stock looks roughly 79.6% undervalued today. We read business quality at 82/100 (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: medium) — always confirm before acting.
About the company
Gestamp Automoción, S.A. designs, develops, and manufactures metal components for the automotive industry in Western Europe, Eastern Europe, Mercosur, North America, and Asia. The company offers body-in-white products, large components and assembly parts, such as bonnets, roofs, doors, and mudguards, as well as other surface and assembly parts for use in creation of visible exterior skin of vehicles; and structural and crash-related elements, including floors, pillars, rails, and wheel arches. It also provides chassis parts comprising under body of vehicles and includes systems, frames and related parts, such as front and rear axles and couplings, control arms, and integrated couplings; hinges, door checks, electrical systems, and powered systems. In addition, the company offers hydraulic presses and dies in the areas of hot stamping, cold stamping, hydroforming, and try out of tools. Further, it serves vehicle manufacturers. Gestamp Automoción, S.A. was incorporated in 1997 and is …
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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.