eCloudvalley Digital Technology Co (6689) Fair Value & Analysis
Technology · TW · Market cap 4.6B TWD
Fair value as of: Jun 24, 2026
Analysis
eCloudvalley Digital Technology Co (6689) currently trades at 66.60 TWD, while our model-based Fair Value estimate is 40.10 TWD — implying the stock looks roughly 39.8% overvalued today. We read business quality at 90/100 (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
eCloudvalley Digital Technology Co., Ltd. provides cloud information services in Asia and internationally. The company provides cloud services comprising public cloud; next-generation management services; Atlas, a cloud management platform to help customers deploy cloud resources, optimize cloud performance, and reduce cost; cloud migration solutions; and a content delivery network. It offers cloud native development; FinOps; and sustainability and carbon management. In addition, the company provides cloud security on Amazon Web Services (AWS); information security testing services; cloud security services; agent as a service; data modernization; and AI and machine learning solutions. Further, it offers other services, such as well-architected review services, training courses on Amazon Web Services (AWS) cloud capabilities; eCloudture cloud talent program. Additionally, the company provides ERP; customer relationship management; database solutions; SAP software systems; Oracle, a d…
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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.