Funeng Oriental Equipment Technology Co (300173) Fair Value & Analysis
Industrials · CN · Market cap 2.7B CNY
Fair value as of: Jun 24, 2026
Analysis
Funeng Oriental Equipment Technology Co (300173) currently trades at ¥3.54, while our model-based Fair Value estimate is ¥0.7100 — implying the stock looks roughly 79.9% 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
Funeng Oriental Equipment Technology Co., Ltd. engages in the research and development, production, sale, and service of lithium battery automated production equipment in China and internationally. The company offers cutting and stacking machines, welding and x-ray inspection machines, packaging machines, vacuum tunnel furnaces, liquid injection machines and logistics lines, degassing and final sealing machines, EV square power assembly lines, and equipment suitable for lithium battery production lines, as well as for new energy power and storage, consumer digital, and other technology batteries. It also provides die-cutting processing products, such as conductive/insulating products, double-sided adhesive products, graphene and foam products, mesh and protective film products, and heat sinks and dissipation films for bonding, isolation, conduction, protection, and heat dissipation. In addition, the company offers internet data center (IDC) data storage and operation services, inclu…
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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.