FLSmidth & Co (FLIDF) Fair Value & Analysis
Industrials · US · Market cap $4.3B
Analysis
FLSmidth & Co (FLIDF) currently trades at $78.86, while our model-based Fair Value estimate is $41.67 — implying the stock looks roughly 47.2% 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
FLSmidth & Co. A/S provides flowsheet technology and service solutions for the mining industries in Denmark, the United States of America, Canada, Chile, Brazil, Peru, Australia, North and South America, Europe, the Middle East, Africa, and Asia. The company operates through three segments: Service; Products; and Pumps, Cyclones & Valves (PC&V). The company offers mineral processing equipment, such as hydrocyclones and REFLUX classifiers; cone and roll crushers, crushing stations, gyratory and jaw crushers, and sizers; centrifuges, thickeners and clarifiers, and filters; apron and belt feeders, buffalo and vibrating feeders, mine hoists, hybrid apron feeders, and mine shaft equipment; and attrition scrubbers, column and forced-air flotation cells, mixed and self-aspirated flotation cells, and REFLUX and coarse flotation cells. It also includes ball and vertical fine grinding mills, and SAG and AG mills; laboratory equipment, such as automated laboratories, crushing, dividing, drying…
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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.