Hollyland (China) Electronics Technology Corporation (002729) Fair Value & Analysis
Technology · CN · Market cap 3.3B CNY
Analysis
Hollyland (China) Electronics Technology Corporation (002729) currently trades at ¥19.55, while our model-based Fair Value estimate is ¥5.27 — implying the stock looks roughly 73.0% overvalued today. We read business quality at 95/100 (high quality), in the Technology 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
Hollyland (China) Electronics Technology Corporation Limited engages in the research and development, production, and sale of circuit protection products in China and internationally. It provides new energy solutions, such as energy storage solutions, photovoltaic solutions, wind power solutions, electric vehicle solutions, charging station solutions, and lithium battery protection solutions. The company also offers smart home solutions, household appliances, intelligent kitchen and bathroom, lighting series, and power supply; and digital solutions, including data centers and intelligent manufacturing. In addition, the company provides special fuse for new energy, axial lead fuse, surface mount fuse, board type bolt fuse, three terminal fuse, and other types of fuses. The company was formerly known as Xiamen Ningly Electronics Company Limited and changed its name to Hollyland (China) Electronics Technology Corporation Limited in July 2010. Hollyland (China) Electronics Technology Co…
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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.