Tianjin Meiteng Technology Co (688420) Fair Value & Analysis
Industrials · CN · Market cap 2.0B CNY
Fair value as of: Jun 24, 2026
Analysis
Tianjin Meiteng Technology Co (688420) currently trades at ¥22.39, while our model-based Fair Value estimate is ¥12.75 — implying the stock looks roughly 43.1% overvalued today. We read business quality at 93/100 (high quality), in the Industrials 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
Tianjin Meiteng Technology Co., Ltd engages in the research, development, design, manufacture, and sale of coal and mineral sorting equipment, intelligent systems and instrument, and other related intelligent management systems in China. The company offers Intelligent sorting and equipment products, such as intelligent dry separator, intelligent photoelectric sorter, intelligent ladder flow dry separator, intelligent coarse slime separator, intelligent small particle size dry separator, and digital converter cyclone. It also provides intelligent systems for the production process, production management system, intelligent electromechanical management, and intelligent shipping integrated management platform. In addition, the company offers smart instruments, x-ray induces ash meter, x-ray induces slurry ash meter, tailings image analyzer, smart dosing station, visual mechanical isolator VMI, sorting effect detector, and the eye of wisdom, as well as offers cloud open management platf…
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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.