Ronglian Group (002642) Fair Value & Analysis
Technology · CN · Market cap 4.7B CNY
Analysis
Ronglian Group (002642) currently trades at ¥6.77, while our model-based Fair Value estimate is ¥1.25 — implying the stock looks roughly 81.5% 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
Ronglian Group Ltd. provides information technology (IT) services in China and internationally. It provides system integration services, including data center construction, data storage, disaster recovery and backup, network and information security, intelligent buildings, security systems, AI artificial intelligence applications, software customization and development, etc., as well as develops biotechnology and information technology. The company also offers IT services, such as consulting planning, operation and maintenance management, business outsourcing, software implementation and development, computer room relocation, system and data migration, cloud hosting, cloud management services, etc.; Internet of Things solutions comprises energy consumption monitoring, new energy vehicle monitoring, intelligent building operation and control, and integrated collaborative management and control solutions for smart mines; and big data services. It is involved in the provision of teleco…
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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.