China Tianying Inc (000035) Fair Value & Analysis
Industrials · CN · Market cap 11.9B CNY
Analysis
China Tianying Inc (000035) currently trades at ¥5.24, while our model-based Fair Value estimate is ¥3.28 — implying the stock looks roughly 37.4% overvalued today. We read business quality at 94/100 (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
China Tianying Inc. engages in urban environmental service and energy businesses in China and internationally. The company offers design research services, such as industry research, planning construction, engineering design, emerging technologies, and monitoring and analysis services; and waste classification and collection, urban cleaning, and recyclables collection. It also provides disposal and recycling services for circular economy industrial park and waste to energy plants, as well as hazardous waste disposal, construction and demolition waste treatment, food waste treatment, and leachate and waste treatment services; and equipment manufacturing and urban service cloud platform. In addition, the company offers gravity energy storage, renewable generation, and projects integrating wind, solar, storafe, H1, NH3, and MeOH. The company was formerly known as China Kejian Co., Ltd. and changed its name to China Tianying Inc. in June 2014. The company was founded in 1984 and is head…
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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.