Jiuzhitang Co (000989) Fair Value & Analysis
Healthcare · CN · Market cap 6.8B CNY
Analysis
Jiuzhitang Co (000989) currently trades at ¥7.95, while our model-based Fair Value estimate is ¥5.72 — implying the stock looks roughly 28.1% overvalued today. We read business quality at 84/100 (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: medium).
About the company
Jiuzhitang Co., Ltd. provides traditional Chinese, chemical, biological, and health medicine products in China. It offers Shuxuetong injections, Angong Niuhuang pills, Tianma Gouteng granules, Danxi granules, and Compound Lipid-Reducing tablets for cardiovascular and neurological treatment; Nude Flower Zizhu tablets, Xiaojin pills, Guizhi Fuling pills, Buxue Shengru granules, and Zhixue Zhentong capsules for gynecological treatment; Houyan pills for ear, nose, and throat (ENT) diseases; Jianwei Yuyang tablets for digestion; and prescription drug product systems, Jiaxi Ganxian granules, and Chidan Tuihuang granules for liver diseases. The company also provides over-the-counter (OTC) drugs comprising blood-tonifying, cardiovascular and cerebrovascular, urinary system, pediatric, gynecologic, bone and muscles, respiratory system, dermatologic, nervous system, ENT, and digestive system products, and stem cell and other drugs. Jiuzhitang Co., Ltd. was founded in 1999 and is headquartered…
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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.