Sinoma Energy Conservation Ltd (603126) Fair Value & Analysis
Utilities · CN · Market cap 4.6B CNY
Analysis
Sinoma Energy Conservation Ltd (603126) currently trades at ¥7.25, while our model-based Fair Value estimate is ¥2.16 — implying the stock looks roughly 70.2% overvalued today. We read business quality at 86/100 (high quality), in the Utilities 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
Sinoma Energy Conservation Ltd. operates in the energy conservation and environmental protection business in China and internationally. The company engages in general engineering contracting, equipment manufacturing and sales; energy-saving and environmental protection engineering and equipment; building energy-saving materials; and is involved in the research, development, manufacture, and sale of fiber cement panel/calcium silicate panels and aerated concrete block/panel production line sets. It is involved in waste heat power generation; geothermal development; electricity and heat production and supply; professional technical services; clean energy power generation; low-carbon energy cycles; energy storage systems; and integrated green energy management. Sinoma Energy Conservation Ltd. was founded in 1998 and is based in Tianjin, China. Sinoma Energy Conservation Ltd. operates as a subsidiary of China National Building Material Group Co., Ltd.
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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.