Kaishan Group (300257) Fair Value & Analysis
Industrials · CN · Market cap 27.8B CNY
Fair value as of: Jun 24, 2026
Analysis
Kaishan Group (300257) currently trades at ¥27.46, while our model-based Fair Value estimate is ¥6.98 — implying the stock looks roughly 74.6% 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
Kaishan Group Co., Ltd. researches, develops, manufactures, and sells compressor products in China and internationally. The company offers screw air compressors, mobile screw air compressor, centrifugal compressors, scroll compressors, fluid machinery, refrigeration equipment, expansion power equipment, process gas compressors, screw chillers, and purification equipment, as well as geothermal power generation, industrial waste heat and pressure power generation, and biomass power generation equipment. It also involved in the geothermal resource exploration and development, drilling engineering, geothermal resource management, and geothermal power station construction activities. The company was formerly known as Zhejiang Kaishan Compressor Co., Ltd. and changed its name to Kaishan Group Co., Ltd. Kaishan Group Co., Ltd. was founded in 1956 and is headquartered in Shanghai, China. Kaishan Group Co., Ltd. is a subsidiary of Kaishan Holding 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.