Ton Yi Industrial Corp (9907) Fair Value & Analysis
Consumer Cyclical · TW · Market cap 26.4B TWD
Fair value as of: Jun 24, 2026
Analysis
Ton Yi Industrial Corp (9907) currently trades at 15.05 TWD, while our model-based Fair Value estimate is 24.83 TWD — implying the stock looks roughly 65.0% undervalued today. We read business quality at 91/100 (high quality), in the Consumer Cyclical sector. Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: high) — always confirm before acting.
About the company
Ton Yi Industrial Corp. manufactures and sells tinplate and plastic packaging materials in Taiwan, Mainland China, and internationally. The company provides tin mill black plates and cold rolled steel of thin gauge for use in the electric and electronic parts, appliances, steel furniture, containers, structural materials, etc.; tinplates and TFS products which are used in food packing materials, motor oil cans, electric parts, dry cells, stations, toys, etc.; and tin and metal cans for various food and beverages. It offers beverages pet products for tea drink, vegetable and fruit juice, milk tea, coffee, sporty drinks, and vegetable protein drinks, as well as sales and technical services; beverage filling services for production of bottle can; and tetra packs for tea, juice, milk tea, coffee, soy milk and other products. The company was formerly known as Ton Yi Agriculture Industrial Corporation and changed its name to Ton Yi Industrial Corp. in 1974. The company was incorporated in…
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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.