Shenyang Blue Silver Industry Automation Equipment Co (300293) Fair Value & Analysis
Industrials · CN · Market cap 6.1B CNY
Analysis
Shenyang Blue Silver Industry Automation Equipment Co (300293) currently trades at ¥18.25, while our model-based Fair Value estimate is ¥10.41 — implying the stock looks roughly 43.0% 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
Shenyang Blue Silver Industry Automation Equipment Co., Ltd engages in the industrial cleaning and surface treatment, and industrial intelligent equipment manufacturing businesses in China. The company is involved in researching, developing, producing, and selling cleaning technology applications and surface treatment field equipment products, systems, and solutions. It also provides general multi-piece cleaning and special single-piece cleaning equipment, and precision cleaning systems for medical, precision optics, and high-tech cleaning industries. In addition, the company offers rubber intelligent equipment comprising all-steel radial tire one-step forming machine, semi-steel passenger radial tire one-step forming machine, semi-steel two-drum automatic bead heat bonding machine, all-steel four-drum fully automatic bead hot laminating machine, automatic tread assembly device, pressure and exhaust line device, and other equipment; robot technology and automation technology product…
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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.