Feerum S.A (FEE) Fair Value & Analysis
Industrials · PL · Market cap 170M PLN
Fair value as of: Jun 24, 2026
Analysis
Feerum S.A (FEE) currently trades at 17.15 PLN, while our model-based Fair Value estimate is 13.99 PLN — implying the stock looks roughly 18.4% overvalued today. We read business quality at 92/100 (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: medium).
About the company
Feerum S.A. engages in the production, sale, and assembly of grain dryers, silos, and other equipment for agricultural production storage and drying complexes. The company offers general contracting, professional support, and consulting services; flat-bottomed, hopper-bottomed, hopper-bottomed with internal basket and open funnel, forwarding, square, non-standard, with assimmetric cone, with sided discharge, with flat metal sheet lining, and grain cooler silos; and continuous flow, SG, FTD, and batch flow dryers. It also provides air separators; cleaners, including control panel with inverter, aspiration of the sieve drum, cyclone, fan, and air channels; automatics; bucket elevators, straight chain conveyors, belt conveyors, screw conveyors, silo screw auger, supporting structure, as well as arched, diagonal, z-type chain conveyors; and sheds, and halls and warehouses. In addition, the company offers electric, pneumatic, and manual distributors; pipes, elbows, reductions, symmetrica…
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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.