Shanghai Shunho New Materials Technology Co (002565) Fair Value & Analysis
Consumer Cyclical · CN · Market cap 14.3B CNY
Analysis
Shanghai Shunho New Materials Technology Co (002565) currently trades at ¥12.65, while our model-based Fair Value estimate is ¥1.29 — implying the stock looks roughly 89.8% overvalued today. We read business quality at 85/100 (high quality), in the Consumer Cyclical 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
Shanghai Shunho New Materials Technology Co.,Ltd. develops, manufactures, and sells environmental packaging materials in the People's Republic of China. The company provides vacuum aluminized, laser anti-counterfeiting, and other environmentally friendly paper materials for use in industries, such as terminal and social consumer goods; and printed products, such as trademark papers, labeling papers, gift wrapping papers, gift boxes, and handbags. It also offers functional organic fertilizers, as well as soil remediation and treatment services; industrial chemical products and skin care products; and electronic cigarettes. The company was formerly known as Shanghai Luxin Packing Materials Science & Technology Co., Ltd. and changed its name to Shanghai Shunho New Materials Technology Co.,Ltd. in December 2016. Shanghai Shunho New Materials Technology Co.,Ltd. was founded in 2004 and is headquartered in Shanghai, the People's Republic of China.
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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.