Ascletis Pharma Inc (ASCLF) Fair Value & Analysis
Healthcare · US · Market cap $1.4B
Analysis
Ascletis Pharma Inc (ASCLF) currently trades at $1.35, while our model-based Fair Value estimate is $1.22 — implying the stock looks roughly 9.6% overvalued today. We read business quality at 95/100 (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: low).
About the company
Ascletis Pharma Inc., a biotechnology company, engages in the research and development, manufacture, marketing, and sale of pharmaceutical products in Mainland China. The company's commercial products include Ritonavir tablet; and ASCLEVIR and GANOVO for use in the treatment of Hepatitis C virus. It is also developing ASC22 for treating CHB and HIV functional cure; ASC10 for treating respiratory syncytia virus; ASC10 and ASC11 to treat COVID-19; ASC40, ASC41, and ASC43F FDC for non-alcoholic steatohepatitis; and ASC42 for the treatment of primary biliary cholangitis. In addition, the company is developing ASC40 to treat recurrent glioblastoma; ASC61 for advanced solid tumors; and ASC40, an oral small molecule for the treatment of acne. Further, it offers ASC30, for obesity; ASC47 for muscle-preserving weight loss treatment; and ASC39, a potent and amylin-selective oral small molecule amylin receptor agonist. Ascletis Pharma Inc. was founded in 2013 and is based in Wan Chai, Hong Kong.
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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.