Hangzhou Gisway Information Technology Co (301390) Fair Value & Analysis
Technology · CN · Market cap 1.8B CNY
Fair value as of: Jun 24, 2026
Analysis
Hangzhou Gisway Information Technology Co (301390) currently trades at ¥29.78, while our model-based Fair Value estimate is ¥8.20 — implying the stock looks roughly 72.5% 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
Hangzhou Gisway Information Technology Co.,Ltd. provides power engineering technical and geographic information technology services in China. The company operates through three segments: 3DGIS Smart Services, Integrated Power Services, and Integrated Energy Services. It offers 3DGIS engine that provides intelligent geographic information data and application services, including distribution network auxiliary design, 3D demonstration function, and engineering information application. The company also engages in the planning consultation, design, engineering construction, operation, and maintenance services; and offers power engineering general and professional contracting, power planning consulting, and power engineering survey and design services to power grid companies, government agencies, and industrial and commercial enterprises. In addition, it provides integrated energy management, power distribution monitoring and maintenance, energy efficiency management, and virtual power p…
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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.