Liaoning Fu-An Heavy Industry Co (603315) Fair Value & Analysis
Basic Materials · CN · Market cap 5.2B CNY
Analysis
Liaoning Fu-An Heavy Industry Co (603315) currently trades at ¥16.99, while our model-based Fair Value estimate is ¥3.69 — implying the stock looks roughly 78.3% overvalued today. We read business quality at 95/100 (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: high).
About the company
Liaoning Fu-An Heavy Industry Co.,Ltd produces and sells steel castings in China. It offers casting products, such as alloy steel, ordinary steel, ductile iron, gray cast iron, and various types of sheet metal structural parts, which are widely used in energy and power, transportation, ships, rolling machinery, mining machinery, coal machinery, petroleum and chemical machinery, and other industries. The company also develops small and medium-sized gas turbines used in distributed energy; designs, produces, sells, and after-sales service of gas turbine units; and provides installation and commissioning of gas turbine units, as well as training, maintenance and repair, and spare parts supply. In addition, it provides heavy machinery and equipment, including mining mills, crushers, mobile semi-mobile crushing stations, various beneficiation equipment, stockyard equipment, sintering, pelletizing, and ring equipment; coal grinding equipment; and cement equipment for power stations. Furth…
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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.