Kansai Paint Co (KPTCY) Fair Value & Analysis
Basic Materials · US · Market cap $2.8B
Analysis
Kansai Paint Co (KPTCY) currently trades at $8.02, while our model-based Fair Value estimate is $9.50 — implying the stock looks roughly 18.5% undervalued today. We read business quality at 82/100 (high quality), in the Basic Materials 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: medium) — always confirm before acting.
About the company
Kansai Paint Co., Ltd., together with its subsidiaries, manufactures and sells paints and coatings in Japan, India, China, Asia, Africa, Europe, and internationally. The company offers decorative coatings for protecting exterior and interior of buildings; automotive refinish coatings for dents and scrapes; and industrial coatings that are used in industrial vehicles, steel furniture, agricultural machinery, special-purpose vehicles, exterior building materials, metal products, beverage cans, and precoated metals used in metallic building materials and home appliances. It also offers marine coatings; automotive coatings for vehicles, including new car body, plastic parts, and aluminum wheel painting, and automotive color design; and anticorrosion paint for bridges, power equipment, industrial plants, and steel structures, as well as offers fire-resistant coatings. In addition, the company provides coatings solutions through electronic materials coating design technologies; and develo…
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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.