Teijin Limited (TINLY) Fair Value & Analysis
Industrials · US · Market cap $1.8B
Fair value as of: Jun 26, 2026
Analysis
Teijin Limited (TINLY) currently trades at $9.35, while our model-based Fair Value estimate is $3.13 — implying the stock looks roughly 66.5% overvalued today. We read business quality at 96/100 (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
Teijin Limited engages in the fibers, films and sheets, composites, healthcare, and IT businesses in Japan and internationally. The company provides aramid fibers; glass and carbon fibers, composite materials, infusion resins, adhesives, fillers and resin films, and oxidized PAN fibers; polycarbonate sheets and films; high-density polyethylene porous films and materials; and microporous films. It also offers PC resins, polyphenylene sulfide resins, molded parts, flame retardant, and additives; lightweight glass and carbon fiber reinforced composites for automotive applications; and polytrimethylene telephthalate products, artificial leather materials, polyester nanofibers, and recycled polyester fibers. In addition, the company provides pharmaceuticals for bone and joint, respiratory, cardiovascular and metabolic, and other diseases; home oxygen therapy, non-invasive positive pressure ventilation, continuous positive airway pressure, respiratory support, and sleep disordered breathi…
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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.