SinterCast AB (SINT) Fair Value & Analysis
Industrials · SE · Market cap 771M SEK
Fair value as of: Jun 24, 2026
Analysis
SinterCast AB (SINT) currently trades at kr 101.50, while our model-based Fair Value estimate is kr 61.46 — implying the stock looks roughly 39.4% 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
SinterCast AB (publ) provides process control technology to produce compacted graphite iron (CGI) for the foundry and automotive industries in Brazil, Mexico, Sweden, the United States of America, Korea, China, Spain, Japan, the United Kingdom, and internationally. The company offers the fully automated System 4000, which comprises individual hardware modules that can be configured to suit the layout, process flow, and production volume of a foundry for ladle production and pouring furnaces; and a hardware and software platform that enables the foundry to independently control CGI series production and product development. It also provides Mini-System 4000, a purpose-built thermal analysis system for product development, and niche volume production. In addition, the company offers sampling consumables that consist of sampling cups and thermocouple pair products; and engineering services for product development, trials, new installations and calibrations, metallurgical consultancy, a…
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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.