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Xiamen Sunrise Group (002593) Fair Value & Analysis

Consumer Cyclical · CN · Market cap 3.6B CNY

Price¥4.30
Fair Value¥3.01
Upside-30.0%
Quality95/100
Evidence: High Range ¥2.26 – ¥3.81

Analysis

Xiamen Sunrise Group (002593) currently trades at ¥4.30, while our model-based Fair Value estimate is ¥3.01 — implying the stock looks roughly 30.0% 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: high).

About the company

Xiamen Sunrise Group Co., Ltd. engages in the design, research and development, production, and sale of automotive wheels and steel structures in China and internationally. The company offers car wheel products, such as tube steel wheels, tubeless steel wheels, and forged aluminum. It is also involved in the design, manufacture, and installation of steel structures, as well as metal enclosure systems; petrochemical and equipment steel structure engineering, municipal administration and bridge and rail transit, large-scale high-end industrial plants, and high-rise steel structure buildings, and steel structure manufacturing of petroleum refining and chemical industries. The company was formerly known as Xiamen Sunrise Wheel Group Co. Ltd. and changed its name to Xiamen Sunrise Group Co., Ltd. in June 2015. Xiamen Sunrise Group Co., Ltd. was founded in 1995 and is headquartered in Xiamen, China.

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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.