Taier Heavy Industry Co (002347) Fair Value & Analysis
Industrials · CN · Market cap 3.3B CNY
Analysis
Taier Heavy Industry Co (002347) currently trades at ¥6.28, while our model-based Fair Value estimate is ¥1.84 — implying the stock looks roughly 70.7% overvalued today. We read business quality at 95/100 (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
Taier Heavy Industry Co., Ltd. designs, develops, manufactures, markets, and services metallurgical equipment and spare parts in China. The company provides universal joint shafts, gear couplings, shear blades, sliding plates, mandrels; rolling mill housing frame and laser remanufacturing of drive shaft, stepped plate and other product series; and cold-rolled hot-dip galvanized shaft sleeve and bush, sink roll and stabilizing roll series, furnace roll, process roll, side guide, laminar colling roll, and crystallizer for continuous casting. It also offers wire rod coil compactors; bar bundling, H-beam tying, strapping, and spray marking machines; and de-coilers/re-coilers, as well as compactor spare parts, and cold and hot rolling equipment. The company was formerly known as Anhui Taier Heavy Industry Co., Ltd. and changed its name to Taier Heavy Industry Co., Ltd. in February 2013. Taier Heavy Industry Co., Ltd. was founded in 2000 and is based in Maanshan, China.
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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.