Sinosteel New Materials Co (002057) Fair Value & Analysis
Technology · CN · Market cap 8.8B CNY
Fair value as of: Jun 25, 2026
Analysis
Sinosteel New Materials Co (002057) currently trades at ¥11.96, while our model-based Fair Value estimate is ¥7.84 — implying the stock looks roughly 34.4% overvalued today. We read business quality at 83/100 (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
Sinosteel New Materials Co., Ltd. research and development, manufacturing of magnetic materials, magnetic separation, metal products and mining equipment, and materials and engineering inspection and testing services in China and internationally. It offers ferrite magnet for automobile motors, household appliance motors, and DC permanent magnet motors; neodymium magnets, including sintered NdFeB magnets and multi-poles ring magnets; and manganous-manganic oxide. The company also provides permanent wet drum magnetic separators, drum permanent magnetic separators, multi-poles rising magnetic separators, dry permanent magnetic separators, roll type high intensity permanent magnetic separators, deironing magnetic separators, and permanent magnetic dehydration tanks. In addition, it offers high press grinding rollers; metal tower mills; and motors. The company was formerly known as Sinosteel Anhui Tianyuan Technology Co.,Ltd. Sinosteel New Materials Co., Ltd. was founded in 2002 and is b…
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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.