OPT Machine Vision Tech Co (688686) Fair Value & Analysis
Technology · CN · Market cap 15.2B CNY
Analysis
OPT Machine Vision Tech Co (688686) currently trades at ¥153.03, while our model-based Fair Value estimate is ¥30.00 — implying the stock looks roughly 80.4% overvalued today. We read business quality at 80/100 (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: medium).
About the company
OPT Machine Vision Tech Co., Ltd., together with its subsidiaries, engages in research, development, production, and sale of core machine vision software and hardware, and motion control products in Mainland China and internationally. The company offers machine vision systems, comprising machine vision software, SKD, and vision controllers; smart and industrial cameras; smart code readers; 3D cameras; and industrial lenses, consisting of line scan, fixed focal, telecentric, microscopic, liquid, and special lenses. It also provides standard and custom light sources; light source controllers; and measurement system products. In addition, the company offers 3D laser profilers and barcode readers; and after-sales services. Its products are used in semiconductors, display panels, 3C products, battery technology and renewable energy, laser processing, automotive, pharmaceutics, household chemicals, food and beverage, packaging and printing, tooling and machining, and plastic products appl…
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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.