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Materials Analysis Technology Inc (3587) Fair Value & Analysis

Technology · TW · Market cap 20.5B TWD

Price305.50 TWD
Fair Value121.26 TWD
Upside-60.3%
Quality95/100
Evidence: High Range 87.45 TWD – 156.86 TWD

Analysis

Materials Analysis Technology Inc (3587) currently trades at 305.50 TWD, while our model-based Fair Value estimate is 121.26 TWD — implying the stock looks roughly 60.3% 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

Materials Analysis Technology Inc. engages in the research and development, and intellectual property services in Taiwan, Mainland China, Japan, the United States, and internationally. It offers reliability, non-destructive, electrical failure, materials, surface, physicochemical, and nano solution analysis, as well as circuit edit, sample preparation, and total solution services, including FA flow, benchmark analysis, and project services. The company also provides educational support and related technical consultation services; and researches for, develops, manufactures, and trades new electronic components and testing technologies. It serves integrated circuit, flat-screen display, optoelectronics, testing and packaging, nano components and materials industries, and compound semiconductors. Materials Analysis Technology Inc. was founded in 2002 and is headquartered in Hsinchu City, 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.