Yingkou Jinchen Machinery Co (603396) Fair Value & Analysis
Industrials · CN · Market cap 4.0B CNY
Analysis
Yingkou Jinchen Machinery Co (603396) currently trades at ¥28.15, while our model-based Fair Value estimate is ¥24.75 — implying the stock looks roughly 12.1% overvalued today. We read business quality at 93/100 (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
Yingkou Jinchen Machinery Co., Ltd. focuses on vacuum coating technology, automation technology, and equipment intelligent solutions in China and internationally. The company offers solar cell equipment and solutions, including PERTOP high-efficiency and heterojunction solar cell tube PECVD solutions; solar cell injection, and PL and PE machines; silicon wafer crack detection systems; AOI testing equipment for solar cell; diffusion/thermal oxide/annealing, ALD, texturing process, wet etching, and plate PECVD loading and unloading machines; and offline cassette loading automation machines. It also provides solar module equipment and solutions, such as automatic taping and gluing, junction box soldering, multi-function busbar soldering, cell string automatic layup, solar cell shingling, EVA/TPT automatic cutting, EVA flatting, solar module EL/AOI, automatic dispensing and framing, automatic trimming, lifters, automatic frame-placement and framing, sand belt corner grinding, and solar …
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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.