China Fangda Group (200055) Fair Value & Analysis
Industrials · CN · Market cap HK$1.4B
Fair value as of: Jun 24, 2026
Analysis
China Fangda Group (200055) currently trades at HK$1.29, while our model-based Fair Value estimate is HK$0.6400 — implying the stock looks roughly 50.4% 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
China Fangda Group Co., Ltd. engages in the production and sales of curtain wall materials in China. It operates in five segments: Curtain Wall, Rail Transit, Real Estate, New Energy, and Others. The company produces and sales curtain wall materials for high-end office towers, corporate headquarters, urban complexes, hotels, large-scale venues, urban public buildings, and government administrative buildings; as well as luxury residentials; and design, production, and installation of building curtain walls, as well as provision of curtain wall testing and maintenance services. It also provides assembly and processing of subway screen doors, screen door detection, and maintenance services; and property lease and management services, as well as develops and operates real estate on land. In addition, the company is involved in photovoltaic power generation; sale of photovoltaic power plant; research and development, installation, and sale of photovoltaic equipment; and design and instal…
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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.