Wuhan Tianyuan Group (301127) Fair Value & Analysis
Industrials · CN · Market cap 8.3B CNY
Fair value as of: Jun 24, 2026
Analysis
Wuhan Tianyuan Group (301127) currently trades at ¥16.98, while our model-based Fair Value estimate is ¥5.08 — implying the stock looks roughly 70.1% overvalued today. We read business quality at 92/100 (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
Wuhan Tianyuan Group Co., Ltd. provides environmental treatment services. It is involved in the water environment treatment, solid waste disposal, environmental protection energy, municipal infrastructure construction, leachate treatment, sewage treatment, and water environment governance and services. The company also engages urban and rural sewage treatment, industrial wastewater treatment, urban water supply, reclaimed water reuse, treatment of highly difficult organic wastewater, municipal engineering, and water ecological restoration; municipal solid waste incineration power generation, comprehensive sludge treatment, disposal of decommissioned new energy devices, fly ash disposal and resource utilization; wind power generation, photovoltaic power generation, energy storage, hydrogen energy, green ammonia, green methanol, and energy performance contracting; computing power, artificial intelligence, AI big data models, and intelligent supercharging; environmental protection equi…
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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.