Tibet Rhodiola Pharmaceutical Holding (600211) Fair Value & Analysis
Healthcare · CN · Market cap 11.8B CNY
Analysis
Tibet Rhodiola Pharmaceutical Holding (600211) currently trades at ¥37.90, while our model-based Fair Value estimate is ¥51.33 — implying the stock looks roughly 35.4% undervalued today. We read business quality at 86/100 (high quality), in the Healthcare sector. Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: medium) — always confirm before acting.
About the company
Tibet Rhodiola Pharmaceutical Holding Co. produces and sells pharmaceutical products in China and internationally. The company offers modern Tibetan Medicines, including Nodikang capsules and granules, snow mountain golden arhat pain relief film, and ten-flavor Tida capsules; modern Chinese medicines, such as children Shuangqing granules, lipid clearing capsules, ginseng and zhi capsules, puyu capsules, and epilepsy flat film; and recombinant human brain natriuretic peptide for injection. It also provides isosorbide mononitrate sustained-release tablets; and oxygen rhodiola rosea moisturizing essence, secret recipe fragrance powder packet, rhodiola rosea, and Yuan Yang rhodiola rosea moisturizing essence products. In addition, the company offers biopharmaceuticals, traditional Chinese medicine, and chemical drugs for various fields, such as cardiovascular and cerebrovascular diseases, hepatobiliary diseases, orthopedic pain relief, rheumatism, and cold medicines. The company was fou…
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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.