Cordlife GrouCordlife Group (CLIFF) Fair Value & Analysis
Healthcare · US · Market cap $38.6M
Fair value as of: Jun 26, 2026
Analysis
Cordlife GrouCordlife Group (CLIFF) currently trades at $0.1505, while our model-based Fair Value estimate is $0.0400 — implying the stock looks roughly 73.4% 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
Cordlife GrouCordlife Group Limited, an investment holding company, provides cord blood banking services in Singapore, Hong Kong, India, Malaysia, the Philippines, and internationally. It operates in two segments, Banking and Diagnostics. The Banking segment collects, processes, banks, and tests biological materials, such as cord blood, lining, and tissue samples. The Diagnostics segment offers diagnostic testing services, such as newborn genetic and metabolic screenings; pediatric vision and ear screenings; pediatric allergen test; and genetic talent test services. The company also provides corneal lenticule banking, and lifestyle genetic screening services for adults. In addition, it offers life sprouts, medical laboratory, marketing, and property investment services, as well as medical and pharmaceutical research and development and consulting services. Cordlife Group Limited was incorporated in 2001 and is based in Singapore.
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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.