Beijing E-Hualu Information Technology Co (300212) Fair Value & Analysis
Technology · CN · Market cap 7.4B CNY
Fair value as of: Jun 24, 2026
Analysis
Beijing E-Hualu Information Technology Co (300212) currently trades at ¥9.92, while our model-based Fair Value estimate is ¥4.14 — implying the stock looks roughly 58.3% overvalued today. We read business quality at 80/100 (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: low).
About the company
Beijing E-Hualu Information Technology Co., Ltd. engages in the provision of digital capitalization services in China and internationally. The company offers iLake, which provides network disk services for government and enterprise users; Esync, an online data migration tool that offers cross-platform automatic transmission with the functions of mounting, copying to remote, and moving to remote; and city video analysis collection and storage solutions. It also provides Blu-ray storage products; Superstor that provides data hierarchical storage services; Data lifecycle governance; Big Data basic platform which offers Hadoop, Spark, MPP, and other big data storage and computing services; data resource management platform, a data resource development and management tool; city and traffic brain; and YiShu Data Exchange. In addition, the company offers super storage, data resource service, and data capitalization capacity; and blu-ray storage technology, EHL and Huawei joint solution, di…
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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.