Jiangsu Aoyang Health Industry Co. (002172) Fair Value & Analysis
Basic Materials · CN · Market cap 2.4B CNY
Fair value as of: Jun 25, 2026
Analysis
Jiangsu Aoyang Health Industry Co. (002172) currently trades at ¥3.01, while our model-based Fair Value estimate is ¥0.8200 — implying the stock looks roughly 72.8% overvalued today. We read business quality at 94/100 (high quality), in the Basic Materials 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
Jiangsu Aoyang Health Industry Co.ltd. engages in the production and sale of viscose chemical fibers in China and internationally. It engages in investment and development in the health industry; manufacture and sale of viscose fiber and viscose fiber products, degradable fibers, and functional fibers; sale of textile raw materials, textiles, and chemical products; steam heat supply; power production; and self-operated and agency of the import and export of commodities and technologies. The company also offers health management and wellness and pharmaceutical logistics services, as well as distributes pharmaceutical products. In addition, it is involved in the wholesale of pharmaceuticals, medical devices, and other products; processing, purchasing, selling, and cultivating of Chinese medicinal materials; rehabilitation medical services; medical aesthetics; pharmaceutical retail; research and experimental development; and provision of maternal and child health consultation services,…
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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.