Zhuzhou Feilu High-Tech Materials Co (300665) Fair Value & Analysis
Basic Materials · CN · Market cap 2.2B CNY
Fair value as of: Jun 24, 2026
Analysis
Zhuzhou Feilu High-Tech Materials Co (300665) currently trades at ¥9.80, while our model-based Fair Value estimate is ¥5.23 — implying the stock looks roughly 46.6% overvalued today. We read business quality at 94/100 (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: low).
About the company
Zhuzhou Feilu High-Tech Materials Co., Ltd. engages in the research and development, production, and sale of anti-corrosion and protective materials for metal surfaces, non-metal surfaces, concrete surfaces, and new energy fields. in China and internationally. The company operates through Paint sales business; Paint coating integration; Painting construction; and New Energy segments. The company offers anti-corrosion coatings, such as pre coated primer; primer; varnish and topcoat; functional and intermediate coatings; water and solvent based coatings for urban rail vehicles, steel structures, railway passenger vehicles, locomotives, cars, and trucks; and damping and fire redundant. It also provides waterproof and protective products comprising membrane, waterproof floor coating, and interior and exterior wall coatings. In addition, it offers new energy products, including polymer materials for batteries and photovoltaic modules, wind power materials, and engaged in photovoltaic pow…
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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.