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Shuanglin Co (300100) Fair Value & Analysis

Consumer Cyclical · CN · Market cap 16.8B CNY

Price¥30.20
Fair Value¥16.25
Upside-46.2%
Quality92/100
Evidence: High Range ¥11.84 – ¥22.93

Analysis

Shuanglin Co (300100) currently trades at ¥30.20, while our model-based Fair Value estimate is ¥16.25 — implying the stock looks roughly 46.2% overvalued today. We read business quality at 92/100 (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

Shuanglin Co., Ltd. engages in the research and development, manufacture, and sale of automotive components and robotic parts in China and internationally. It offers intelligent drive systems, including HDM, seat motors, electric headrests, automotive lead screws, humanoid robot lead screws and joint modules, new energy power systems, bearing units, and intelligent corner modules. The company also provides precision parts, such as precision molds and automotive interior and exterior parts; new energy power systems comprising electric drive; and wheel bearings. In addition, it offers high-precision gears and worm gears, flat wire three-in-one motors, ball screw bearing units for automotive brakes, and planetary roller screws for humanoid robots. The company was formerly known as Ningbo Shuanglin Auto Parts Co.,Ltd. and changed its name to Shuanglin Co., Ltd. in August 2025. Shuanglin Co., Ltd. was founded in 2000 and is based in Ningbo, 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.