Bajaj Mobility AG (BMAG) Fair Value & Analysis
Consumer Cyclical · AT · Market cap €688M
Analysis
Bajaj Mobility AG (BMAG) currently trades at €18.74, while our model-based Fair Value estimate is €94.13 — implying the stock looks roughly 402.3% 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: low) — always confirm before acting.
About the company
Bajaj Mobility AG, together with its subsidiaries, operates as a motorcycle manufacturer in Europe, North America, Mexico, and internationally. It operates through Motorcycles, Bicycles, and Others segments. The company engages in the development, production, and sale of motorcycles under the KTM, Husqvarna Motorcycles, and the GASGAS brands, as well as motorcycle suspension and components under the WP brand. It also develops, manufactures, and trades e-bicycles and bicycles under the Husqvarna E-Bicycles, FELT, and GASGAS Bicycles brands; and sports cars under the KTM X-BOW brand. In addition, the company provides two-wheelers under the KTM AG brands; and e-bikes. Further, it offers design and development services; and digital solutions in web-based and mobile business applications. The company was formerly known as PIERER Mobility AG and changed its name to Bajaj Mobility AG in January 2026. Bajaj Mobility AG was founded in 1953 and is based in Mattighofen, Austria. Bajaj Mobility…
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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.