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Beijing Si-Tech Information Technology Co (300608) Fair Value & Analysis

Technology · CN · Market cap 4.2B CNY

Price¥12.45
Fair Value¥11.71
Upside-5.9%
Quality94/100
Evidence: Medium Range ¥8.78 – ¥14.64

Analysis

Beijing Si-Tech Information Technology Co (300608) currently trades at ¥12.45, while our model-based Fair Value estimate is ¥11.71 — implying the stock looks roughly 5.9% overvalued today. We read business quality at 94/100 (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 Si-Tech Information Technology Co., Ltd. provides cloud and big data services in China. It offers telecom core business support, cloud-native, big data service, and IoT overall support solutions. The company also provides end-to-end solutions for government and enterprise businesses; smart store solution; 5G service support solution for broadcasting; virtual cloud solution; digital ecosystem platform; intelligent middle platform solutions; and information technology ecosystem. In addition, it is involved in the provision of technology development, technology transfer, technology consulting, and technology services, as well as computer system services, computer repair; sale of electronic products, computers, software, and auxiliary equipment; and operation of telecommunications services. Beijing Si-Tech Information Technology Co., Ltd. was founded in 1995 and is headquartered in Beijing, China.

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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.