Saimo Technology Co (300466) Fair Value & Analysis
Technology · CN · Market cap 4.1B CNY
Analysis
Saimo Technology Co (300466) currently trades at ¥7.54, while our model-based Fair Value estimate is ¥1.10 — implying the stock looks roughly 85.4% 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: medium).
About the company
Saimo Technology Co.,Ltd. provides intelligent detection products and control systems in China. The company's Bulk Handling division offers weighing products, including high precision belt scales, floating scale belt scales, coal weighing feeders, weighing feeders, batching systems, checking equipment, weighing instruments/load cells, and other measuring equipment; sampling systems, such as sweep sampling, auger sampling, sample preparation and analytics, and coal sample transportation systems; and conveyor protection products comprising run off, safety pull, position, tilt, and under speed switches, as well as belt damaged detectors and motion monitor systems. It also provides flexible and bulk packaging lines; multi head and linear weigher, auger filler, and volumetric cup filler; open mouth, valve bag, and premade bag packers, as well as vertical form fill seals, bulk bag packer and dischargers, and vacuum loaders; checkweigher and metal detector inspection products; robotic, low…
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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.