Shaniv Paper Industry Ltd (SHAN) Fair Value & Analysis
Basic Materials · Il · Market cap 627M ILA
Fair value as of: Jun 24, 2026
Analysis
Shaniv Paper Industry Ltd (SHAN) currently trades at 10.05 ILA, while our model-based Fair Value estimate is 5.60 ILA — implying the stock looks roughly 44.3% overvalued today. We read business quality at 89/100 (high quality), in the Basic Materials 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
Shaniv Paper Industry Ltd engages in the manufacture and marketing of paper, cleaning, automotive, and personal care products in Israel and internationally. The company offers toilet paper, kitchen towels, handkerchiefs, and napkins, as well as a new line for the institutional market. It also produces and imports aluminum foils and molds, nylon products, packaging, and various paper products. In addition, the company develops, manufactures, and markets cleaning products, including cleaning agents, products for fabrics and carpets care, fragrances and air purifiers, and products for renovation and home maintenance, insecticides, and pests under the Touch, TNX, and Magic brand names; and car products comprising car fragrances, cleaning products, puncture repair products, oils and additives, industrial aids, auxiliary materials for professionals, hand cleaning creams, maintenance sprays, and others under the Maxol-Max brand. Further, it manufactures and markets personal care products 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.