Judges Scientific plc (JSCIF) Fair Value & Analysis
Technology · US · Market cap $433M
Fair value as of: Jun 24, 2026
Analysis
Judges Scientific plc (JSCIF) currently trades at $65.00, while our model-based Fair Value estimate is $30.43 — implying the stock looks roughly 53.2% overvalued today. We read business quality at 95/100 (high quality), in the Technology 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: high).
About the company
Judges Scientific plc designs, manufactures, and sells scientific instruments and services in the United Kingdom and internationally. It operates through two segments, Materials Sciences and Vacuum. The company provides engineering teaching and research equipment for universities, colleges, schools, and research centres; and research and development systems for food, beverages, edible oils, ingredients and pharmaceuticals industries. It offers cutting edge LED illumination systems for researchers and clinicians; tensile testing, motion control, and specimen cooling for microscopy applications; automated measurement systems for fibres; edge welded metal bellows; and fire testing instruments, such as cone and large-scale calorimeters, NBS smoke chambers and oxygen index test, and related fire testing instruments. In addition, the company offers equipment and software for computer-controlled testing of soils and rocks; high-resolution fibre optic instruments using photon-counting OTDR …
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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.