MYS Group (002303) Fair Value & Analysis
Consumer Cyclical · CN · Market cap 5.5B CNY
Analysis
MYS Group (002303) currently trades at ¥3.68, while our model-based Fair Value estimate is ¥3.17 — implying the stock looks roughly 13.9% overvalued today. We read business quality at 94/100 (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: high).
About the company
MYS Group Co., Ltd. develops, produces, and sells packaging products in China and internationally. The company offers transport, corrugated, fine, heavy, and light packaging products; label products; packaging integration services, such as packaging product design, structural design, packaging solution optimization, third-party procurement of packaging materials, and packaging product logistics distribution, as well as supplier inventory management and on-site auxiliary packaging services; and electronic functional material die-cutting products, such as cushioning pads, shock-absorbing foam, adhesive tape, protective film, dust-proof mesh, conductive insulating film, etc. MYS Group Co., Ltd. serves consumer electronics, liquor, furniture, home appliances, electric hand tools, automobile and new energy vehicle industry chain, medical supplies, food and beverage, e-commerce logistics, express delivery and other industries. MYS Group Co., Ltd. was formerly known as Shenzhen MYS Environ…
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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.