Stadler Rail AG (SRAIF) Fair Value & Analysis
Industrials · US · Market cap $2.8B
Analysis
Stadler Rail AG (SRAIF) currently trades at $28.47, while our model-based Fair Value estimate is $18.93 — implying the stock looks roughly 33.5% overvalued today. We read business quality at 94/100 (high quality), in the Industrials 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
Stadler Rail AG, through its subsidiaries, engages in the manufacture and sale of trains in Switzerland, Germany, Austria, Western and Eastern Europe, the Americas, the CIS countries, and internationally. The company operates through three segments: Rolling Stock; Service & Components; and Signalling. The Rolling Stock segment manufactures various rail vehicles comprising high-speed, intercity, regional trains, city transport, locomotives, and tailor made, as well as passenger coaches, light rails, and trams. The Service & Component segment provides revision, spare parts, vehicle repair, modernization and overhauling, and maintenance services; and supplies vehicle components, such as car bodies or bogies. The Signalling segment develops and distributes various signalling solutions for vehicles and infrastructures. This segment offers various solutions in the areas of train protection, communication-based train control for driverless operation, automatic train operation, driving assi…
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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.