Kunming Yunnei Power Co (000903) Fair Value & Analysis
Consumer Cyclical · CN · Market cap 3.9B CNY
Analysis
Kunming Yunnei Power Co (000903) currently trades at ¥1.96, while our model-based Fair Value estimate is ¥1.52 — implying the stock looks roughly 22.4% overvalued today. We read business quality at 95/100 (high quality), in the Consumer Cyclical 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
Kunming Yunnei Power Co.,Ltd., together with its subsidiaries, engages in the research and development, manufacture, and sale of diesel engines for commercial vehicles, passenger cars, and off-road machinery in the People's Republic of China and internationally. The company offers dual fuel, and natural gas engines; natural gas parts, systems, universal parts, and parts assemblies; and heavy-duty gear oils, antifreezing solutions, CD, CI, CF, and CH diesel engine oils, as well as gas and new energy buses. It also provides frame and carriage assemblies; new nanometer ceramic thermal insulation films for automobiles and buildings, transmittance heat insulation, float glass, and deep processing products; silent and diesel generator sets; remanufactured engines; and agricultural machinery products, including rotary cultivators and hay loaders. In addition, the company provides spare parts; hydrogen fuel cell systems; technical consulting and services; communication products; computer ha…
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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.