NaaS Technology Inc (NAAS) Fair Value & Analysis
Consumer Cyclical · US · Market cap $37.4M
Fair value as of: Jun 24, 2026
Analysis
NaaS Technology Inc (NAAS) currently trades at $3.09, while our model-based Fair Value estimate is $2.64 — implying the stock looks roughly 14.6% overvalued today. We read business quality at 81/100 (high quality), in the Consumer Cyclical 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: low).
About the company
NaaS Technology Inc. provides electric vehicle (EV) charging services in China. It offers online EV charging solutions to charging stations, including mobility connectivity services through Kuaidian; SaaS products, such as traffic support and management, marketing, payment, chargers management, order management, load management, and membership management; and operates full station. The company also provides energy solutions comprising site selection; hardware procurement; engineering, procurement, and construction for charging stations. In addition, it integrates smart photovoltaics energy storage and power charging for station upgrade, as well as offline operational and management services. Further, the company is involved in providing electricity procurement services to charging stations; cloud-based virtual power plant platform to facilitate the establishment of a modern energy system for charging stations. Additionally, it offers other amenities and ancillary services, such as v…
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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.