Beijing Aosaikang Pharmaceutical Co (002755) Fair Value & Analysis
Healthcare · CN · Market cap 11.9B CNY
Analysis
Beijing Aosaikang Pharmaceutical Co (002755) currently trades at ¥12.55, while our model-based Fair Value estimate is ¥5.61 — implying the stock looks roughly 55.3% overvalued today. We read business quality at 93/100 (high quality), in the Healthcare 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
Beijing Aosaikang Pharmaceutical Co., Ltd. engages in the research, development, production, and sale of active pharmaceutical ingredients and formulations in China. It also offers digestive health products, which include pump inhibitors suitable for various digestive tract-related diseases, such as peptic ulcers, reflux esophagitis, and upper gastrointestinal bleeding; and anti-tumor products, including the treatment of non-small cell lung cancer, colorectal cancer, and breast cancer, encompassing targeted therapy, chemotherapy, endocrine therapy, and adjuvant medications. In addition, the company provides anti-infective products comprising a product defense line against bacterial and invasive fungal infections; and the chronic disease field focusing on chronic diseases requiring long-term management consisting of diabetes, osteoarthritis, thrombocytopenia, and iron overload. Beijing Aosaikang Pharmaceutical Co., Ltd. was founded in 1996 and is based in Nanjing, 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.