Jiangsu Teeyer Intelligent Equipment Co (603273) Fair Value & Analysis
Industrials · CN · Market cap 3.1B CNY
Fair value as of: Jun 24, 2026
Analysis
Jiangsu Teeyer Intelligent Equipment Co (603273) currently trades at ¥13.93, while our model-based Fair Value estimate is ¥2.49 — implying the stock looks roughly 82.1% overvalued today. We read business quality at 94/100 (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: high).
About the company
Jiangsu Teeyer Intelligent Equipment Co.,Ltd. engages in the research, design, development, production, and sale of automation equipment and mechanical equipment supporting products in China. It offers raw material preparation section products, including ball mills, belt scale, slurry agitators, slurry storage tanks, jaw crushers, belt conveyors, bucket elevators, powder storage silos, and dust collectors; and automatic steel mesh fabricating and circulating section products, such as mesh welding machines, wax-dipping lifting devices, dowel-inserting and dowel-drawing out hoisters, mesh assembly frame conveyor lines, and anticorrosive liquid dipping and drying systems. The company also provides cutting machines comprising cross cutting machines, vertical cutting machines, cutting cars, tongue and groove milling devices, vacuum suction hoods, tiling hoisters, and top and bottom cleaning systems; and autoclaving section products, such as traction mechanisms, ferry push car before/afte…
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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.