Beijing XIAOCHENG Technology Stock Co (300139) Fair Value & Analysis
Technology · CN · Market cap 12.1B CNY
Fair value as of: Jun 24, 2026
Analysis
Beijing XIAOCHENG Technology Stock Co (300139) currently trades at ¥44.21, while our model-based Fair Value estimate is ¥11.05 — implying the stock looks roughly 75.0% overvalued today. We read business quality at 87/100 (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: medium).
About the company
Beijing XIAOCHENG Technology Stock Co., Ltd, together with its subsidiaries, designs and develops integrated circuits in China and internationally. It offers integrated circuit chips, integrated circuit modules, electric energy meters, power line carrier chips, digital electronic detonator chips and modules, and other technical products. The company also provides power line carrier meter reading systems and power management systems. In addition, it engages in the construction, operation, and management of power grid transformation projects; and construction of distribution network and solar power projects, as well as supply, installation, testing, commissioning, and management of power projects. Further, the company is involved in the gold mining; house rentals; and provision of technical services. The company was formerly known as Beijing Fuxing Xiaocheng Electronic Technology Stock Co., Ltd and changed its name to Beijing XIAOCHENG Technology Stock Co., Ltd in November 2015. Beiji…
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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.