Floridienne S.A (FLOB) Fair Value & Analysis
Industrials · BE · Market cap €573M
Analysis
Floridienne S.A (FLOB) currently trades at €60.00, while our model-based Fair Value estimate is €13.78 — implying the stock looks roughly 77.0% overvalued today. We read business quality at 80/100 (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
Floridienne S.A., through its subsidiaries, operates in the life sciences, food, and chemistry sectors in Belgium and internationally. The Chemicals segment offers plastic additives that are used for stabilizing PVC and fireproofing plastics; and recycles nickel/cadmium, nickel-metal hydride, and lithium-ion batteries, as well as zinc salts for galvanization. The Food segment produces and markets party and gourmet food products, such as snails, scallops, stuffed shellfish, smoked salmon, seafood salads, appetizers puff pastries, ethnic dishes, and cold and hot sauces. The Life Sciences segment produces and markets natural technologies and products for agriculture, hygiene products, health care, and other sectors, as well as provides plant proteases and integrated pest management services. This segment also invests in solutions in the fields of human taste and olfaction. The company offers vinegars, condiments, jams and spreads; and calcium-zinc and organic based stabilizers. Floridi…
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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.