Jinling Pharmaceutical Company (000919) Fair Value & Analysis
Healthcare · CN · Market cap 3.9B CNY
Analysis
Jinling Pharmaceutical Company (000919) currently trades at ¥6.02, while our model-based Fair Value estimate is ¥3.31 — implying the stock looks roughly 45.0% overvalued today. We read business quality at 80/100 (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
Jinling Pharmaceutical Company Limited, together with its subsidiaries, manufactures and sells pharmaceuticals and medical devices in China and internationally. It offers Mailuoning injection and oral solution for the treatment of thromboangiitis and arteriosclerosis obliterans, as well as cerebral and venous thrombosis; Sulifei, a ferrous succinate tablet for the prevention and treatment of iron deficiency anemia; Lentinan injection, an adjuvant therapy for the treatment of malignant tumors; and absorbable gelatin sponges for the treatment of wound bleeding and trauma hemostasis. The company also provides traditional Chinese medicines, chemical medicines, active pharmaceutical ingredients, and pharmaceutical intermediates. In addition, it engages in elderly care services, rehabilitation therapy, psychological counseling, and end-of-life care; hospital management; technical services; and healthcare investment activities. The company was incorporated in 1998 and is headquartered in N…
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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.