Addsino Co (000547) Fair Value & Analysis
Technology · CN · Market cap 31.1B CNY
Analysis
Addsino Co (000547) currently trades at ¥19.66, while our model-based Fair Value estimate is ¥7.29 — implying the stock looks roughly 62.9% overvalued today. We read business quality at 83/100 (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
Addsino Co., Ltd., together with its subsidiaries, provides electronic information technology products to military and civilian industries in China. It offers digital simulation systems, semi-physical simulation systems, simulation equipment, target platforms, etc.; and marine equipment, power equipment, and space information applications. The company also provides military network communication products, including, vehicle/shipborne communication node equipment, UAV communication node equipment, IP switches, microwave relays, and network management/service software; spatial information application; big data business products, such as cybersecurity and information technology equipment; and data security products comprising data backup and recovery, application emergency takeover, and disaster recovery cloud; and document management, information reporting, and meeting management. It serves national ministries and commissions, universities, scientific research institutes, enterprises …
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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.