Zhejiang Tiantie Science and Technology Co (300587) Fair Value & Analysis
Industrials · CN · Market cap 6.3B CNY
Analysis
Zhejiang Tiantie Science and Technology Co (300587) currently trades at ¥4.38, while our model-based Fair Value estimate is ¥5.10 — implying the stock looks roughly 16.4% undervalued today. We read business quality at 93/100 (high quality), in the Industrials sector. Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: low) — always confirm before acting.
About the company
Zhejiang Tiantie Science and Technology Co., Ltd. engages in the provision of noise reduction and vibration mitigation technology for rail, road, level crossing, buildings, and industrial applications in China. The company also involved in the development and manufacture of rubber products. It also offers seismic isolators for buildings, such as natural rubber bearing, lead rubber bearing, high damping rubber bearing, friction pendulum seismic isolation, and simple support for village and town houses; shock absorbing products, including viscous dampers, buckling restrained brace, friction dampers, mild steel damper, viscoelastic damper, and tuned mass damper; drain pipe, such as HDPE double wall corrugated pipe, PE water supply pipe, poly septic tank, and plastic inspection chamber; seismic support and hanger product, including seismic hangers and two-way anti-seismic support hanger; and other seismic isolation devices, such as tensile device, wind resistance device, and building is…
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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.