Cenra Inc (3716) Fair Value & Analysis
Healthcare · TW · Market cap 5.0B TWD
Fair value as of: Jun 24, 2026
Analysis
Cenra Inc (3716) currently trades at 35.25 TWD, while our model-based Fair Value estimate is 41.07 TWD — implying the stock looks roughly 16.5% undervalued today. We read business quality at 95/100 (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: high) — always confirm before acting.
About the company
Cenra Inc. engages in the research and development, manufacture, and sale of generic pharmaceutical and healthcare products for humans and animals. It provides CDMO services for internal and external partners; oral solids, injectables, specialty formulations, and APIs, supported by process development, scale-up, and regulatory support; oral solids, injectables, specialty formulations, and high-purity active pharmaceutical ingredients (APIs), supported by process development, scale-up, and regulatory support; distribution, and marketing for prescription drugs, OTC products, health supplements, personal care products, and medical devices;home care, caregiver training, and consulting for elderly support; develops and manufactures veterinary pharmaceuticals, and animal health products for livestock and companion animals and offers CDMO services for animal health partners. Cenra Inc. was founded in 2024 and is headquartered in Taipei, Taiwan.
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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.