Beijing Sifang Automation Co (601126) Fair Value & Analysis
Industrials · CN · Market cap 56.0B CNY
Analysis
Beijing Sifang Automation Co (601126) currently trades at ¥77.81, while our model-based Fair Value estimate is ¥17.70 — implying the stock looks roughly 77.3% 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: high).
About the company
Beijing Sifang Automation Co.,Ltd supplies power transmission, transformation protection, automation systems, power generation, enterprise power, and power distribution and utilization systems in China and internationally. It provides renewable energy solutions, such as plant and substation system integration, plant automation, plant generation control, reactive power compensation and power quality improvement, simulation and training/digital twin; and conventional energy solutions, hydropower plant automation solutions. The company offers transmission solutions include energy management system, digital substation, protection management system, grid security and stability control system; advanced distribution automation systems, residential and industrial microgrid, power supply and utilization for industrial plant, railway and subway protection and automation, reactive power compensation and power quality improvement, isolated grid solutions. In addition, it provides petroleum and …
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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.