Micro-Mechanics (Holdings) Ltd (MCRNF) Fair Value & Analysis
Technology · US · Market cap $322M
Fair value as of: Jun 24, 2026
Analysis
Micro-Mechanics (Holdings) Ltd (MCRNF) currently trades at $2.45, while our model-based Fair Value estimate is $1.37 — implying the stock looks roughly 44.1% 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
Micro-Mechanics (Holdings) Ltd. designs, manufactures, and markets precision parts and tools for the wafer-fabrication, assembly, and testing processes of the semiconductor industry. The company offers die attach-pick-up products, such as rubber tips, high-temp plastic tools, tungsten carbide die collets, sensor assemblies, and vacuum wand tools; die attach-dispensing products, including dispensing nozzle adaptors, dispense nozzles, pen dispensing assemblies, writing pen nozzle tips, and epoxy stamping tools; and die attach-die ejection products comprising ejector needles, needle holders/pepper pots, and needle holder seals. It also provides wire bonding products consisting of thermosonic bonding products, which include clamps and electronic flame off products; and ultrasonic bonding products, such as clamps, bearing base assemblies, anvils, and wire cutters. In addition, the company offers encapsulation products, including BGA dispensing nozzles, dispensing manifolds, pump screws, …
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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.