Suzhou Yangtze New Materials Co (002652) Fair Value & Analysis
Basic Materials · CN · Market cap 2.5B CNY
Fair value as of: Jun 25, 2026
Analysis
Suzhou Yangtze New Materials Co (002652) currently trades at ¥4.56, while our model-based Fair Value estimate is ¥1.39 — implying the stock looks roughly 69.5% overvalued today. We read business quality at 95/100 (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
Suzhou Yangtze New Materials Co., Ltd. engages in the research, development, production, and sale of functional and decorative materials in China, and internationally. The company offers antistatic and antibacterial color coated sheets for use in chip production, mobile phone assembly, and mobile phone screen manufacturing workshop, cleanroom, freezer, hospital, and operating room applications; anti-VHP oxidation color coated sheets that is used for pharmaceutical and prefabricated mobile hospital applications; and HAS constant resistance boards for use in aluminum smelter, chemical, integrated circuit production room, chemical etching, and extreme cation oxidation applications. It also provides weather resistance and thermal insulation color coated sheets, scratch and wear-resistant color coated sheets, pearlescent series color coated sheets, home appliance PSM boards, and film-faced plywood. In addition, the company offers anodized composite panels, interior and exterior wall deco…
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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.