Beijing InHand Networks Technology Co (688080) Fair Value & Analysis
Technology · CN · Market cap 3.1B CNY
Fair value as of: Jun 24, 2026
Analysis
Beijing InHand Networks Technology Co (688080) currently trades at ¥41.16, while our model-based Fair Value estimate is ¥40.81 — implying the stock looks roughly 0.9% 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
Beijing InHand Networks Technology Co., Ltd. engages in research, development, and application of industrial Internet of Things (IoT) technology. The company provides industrial Internet of Things products, enterprise network products, digital distribution network products, smart vending control system products, and other products. It also offers cloud manager, edge routers, enterprise wireless AP, 5G outdoor unit, and series enterprise switch; cellular router and portable cellular router; cellular modems; iSCADA cloud service; DeviceLive cloud, edge computers, standard edge computers, edge gateways, industrial routers, industrial ethernet switches; and power distribution overhead line monitoring system and xenergy real-time energy management solution. In addition, the company provides AI vending cooler system and smart merchandiser system, as well as vehicle gateway and inconnect remote access services. It offers IoT solutions for business networking, industrial IoT, digital energy…
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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.