Great River Smart Logistics Co (002930) Fair Value & Analysis
Industrials · CN · Market cap 4.4B CNY
Analysis
Great River Smart Logistics Co (002930) currently trades at ¥9.15, while our model-based Fair Value estimate is ¥10.42 — implying the stock looks roughly 13.9% undervalued today. We read business quality at 87/100 (high quality), in the Industrials sector. Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: medium) — always confirm before acting.
About the company
Great River Smart Logistics Co., Ltd., a petrochemical product logistics and comprehensive service provider, offers terminal services to domestic and foreign petrochemical product manufacturers, traders, and end users in China and internationally. The company's services include terminal and jetty operation, chemical warehouse storage, transit and other service, logistics chain management service, and other value-added services. Additionally, it offers liquid/gas storage services, drumming, tank truck parking, freight forwarding, brokerage and supervision services, customs and port declaration services, import and export freight forwarding services, dangerous cargo transportation, goods storage, ship and truck loading and unloading, ship to ship transfer, and Intank transfer services. The company was formerly known as Guangdong Great River Smarter Logistics Co., Ltd. and changed its name to Great River Smart Logistics Co., Ltd. in January 2016. Great River Smart Logistics Co., Ltd. w…
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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.