Shenzhen Leaguer Co (002243) Fair Value & Analysis
Consumer Cyclical · CN · Market cap 8.8B CNY
Analysis
Shenzhen Leaguer Co (002243) currently trades at ¥7.20, while our model-based Fair Value estimate is ¥2.21 — implying the stock looks roughly 69.3% overvalued today. We read business quality at 86/100 (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
Shenzhen Leaguer Co., Ltd. engages in the design, manufacturing, and service provision of plastic packaging solutions for cosmetics, daily necessities, health products, and food in China and internationally. It designs, manufactures, and sells process equipment and precision molds; researches and develops polymer material modification and materials, as well as provides base platform, science and technology innovation operation, and investment incubation services. It is also involved in the filling and mixing of cosmetics; design and manufacturing of information labels; design and manufacturing of lightweight automotive solutions; logistics and distribution; recycling of plastic packaging waste; and new material research and development and production. The company was formerly known as Shenzhen Beauty Star Co., Ltd. and changed its name to Shenzhen Leaguer Co., Ltd. in January 2021. Shenzhen Leaguer Co., Ltd. was founded in 1995 is based in Shenzhen, 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.