Dianguang Explosion-proof Technology Co (002730) Fair Value & Analysis
Industrials · CN · Market cap 11.3B CNY
Analysis
Dianguang Explosion-proof Technology Co (002730) currently trades at ¥32.68, while our model-based Fair Value estimate is ¥3.89 — implying the stock looks roughly 88.1% overvalued today. We read business quality at 91/100 (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
Dianguang Explosion-proof Technology Co.,Ltd. engages in the research and development, production, and sale of explosion-proof electrical appliances, power equipment, and drive equipment in China and internationally. The company offers equipment for mining, explosion-proof monitoring equipment for mining, emergency rescue, monitoring and control, system integration, permanent magnet motors, permanent magnet drums, permanent magnet frequency converters and other products, magnet motors and drums, magnet frequency converters and other products. It also provides intelligent metering equipment, various types of meter boxes, low-voltage distribution cabinets, and other products. In addition, the company offers computing power services, such as computing power infrastructure construction, leasing, and cluster architecture design, as well as operation and maintenance management, and equipment supply chain support services. The company was founded in 1993 and is based in Wenzhou, China. Dia…
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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.