KION GROUP AG (KNNGF) Fair Value & Analysis
Industrials · US · Market cap $7.2B
Analysis
KION GROUP AG (KNNGF) currently trades at $55.11, while our model-based Fair Value estimate is $41.84 — implying the stock looks roughly 24.1% overvalued today. We read business quality at 95/100 (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
KION GROUP AG provides industrial trucks and supply chain solutions in Western and Eastern Europe, the Middle East, Africa, North America, Central and South America, China, and the rest of the Asia Pacific. It operates through Industrial Trucks & Services; and Supply Chain Solutions segments. The company offers forklift and heavy trucks, counterbalance trucks with electric drive and internal combustion engine, intralogistics systems, components and spare parts, narrow aisle trucks, lithium-ion batteries, and industrial and warehouse trucks under the Linde, STILL, Baoli, Fenwick, and OM brands. It also leases and rents industrial trucks and related equipment; and provides maintenance and repair services, fleet management solutions, driver assistance systems, and service options, as well as finance solutions. In addition, the company provides integrated technology and software solutions, automated guided vehicle systems, warehouse automation solutions, palletizers, storage and picking…
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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.