Proteomics International Laboratories Ltd (PIQ) Fair Value & Analysis
Healthcare · AU · Market cap A$22.3M
Fair value as of: Jun 26, 2026
Analysis
Proteomics International Laboratories Ltd (PIQ) currently trades at A$0.1000, while our model-based Fair Value estimate is A$0.0700 — implying the stock looks roughly 30.0% overvalued today. We read business quality at 93/100 (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
Proteomics International Laboratories Ltd operates as a medical technology company with a focus on the area of proteomics in Australia, New Zealand, the United States, Europe, India, and Southeast Asia. It provides PromarkerD, a blood test to predict diabetic kidney disease (DKD). The company's products under development include PromarkerEndo, which has completed clinical validation phase for endometriosis diagnosis; PromarkerEso, which has completed clinical validation trials for esophageal cancer diagnosis; and OxiDx (2-tag), which has completed clinical validation phase for oxidative stress treatment. It is also developing diagnostic tests for that includes DKD - T1D, that is under clinical phase for type 1 diabetes, diabetic retinopathy, and diabetic neuropathy; Asthma and COPD, which has completed proof-of-concept study trials for asthma and chronic obstructive pulmonary disease; Plant Dieback, that is under development phase for phytophthora dieback; Diabetic Retinopathy, whic…
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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.