Alfa Laval AB (ALFA) Fair Value & Analysis
Industrials · SE · Market cap 223B SEK
Analysis
Alfa Laval AB (ALFA) currently trades at kr 546.20, while our model-based Fair Value estimate is kr 371.69 — implying the stock looks roughly 31.9% 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
Alfa Laval AB (publ) provides heat transfer, separation, and fluid handling products and solutions worldwide. The company operates through three divisions: Energy, Food & Pharma, and Ocean. It offers ballast water management systems; oil/gas-fired steam and composite steam boilers, exhaust gas economizers, electric steam boilers, and exhaust gas steam boilers; and exhaust gas cleaning products, as well as decanters and separators. The company also provides sensing and control, cleaning validation, and condition monitoring automation products; hygienic fittings, hygienic tubes, ultrapure fittings, and ultrapure tubes; agitators, tank mixers, and powder mixers; and centrifugal pumps, circumferential piston pumps, rotary lobe pumps, twin screws, three screw pumps, gear pumps, and reciprocating pumps. In addition, it offers rotary jet heads, rotary spray heads, wall mounted cleaning devices for tank cleaning, and static spray balls; and tank covers and accessories, as well as valves. Fu…
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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.