Gansu Shangfeng Materials Co (000672) Fair Value & Analysis
Basic Materials · CN · Market cap 16.3B CNY
Analysis
Gansu Shangfeng Materials Co (000672) currently trades at ¥18.76, while our model-based Fair Value estimate is ¥11.18 — implying the stock looks roughly 40.4% overvalued today. We read business quality at 89/100 (high quality), in the Basic Materials 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
Gansu Shangfeng Materials Co., Ltd. manufactures and sells building materials, and cement and cement clinker products in China. It offers dry powder mortar; clinker, concrete, sand and gravel aggregates, and other building materials; and oil well special cement, which are used in railways, highways, machinery, farms, water conservancy projects, oil wells, or large-scale heavy industrial facilities, as well as urban real estate development and construction and rural markets. The company also engages in the mineral resource mining; power generation; processing and sale of stones; import and export business; manufacture and sale of equipment for environmental protection; waste heat and waste pressure power generation; real estate development; asset management; property management; and equity investment and venture capital business. In addition, it is involved in the technical development and technical consultation of energy-saving and environmental protection equipment; waste utilizati…
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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.