Shengfeng Development Limited (SFWL) Fair Value & Analysis
Industrials · US · Market cap $75.1M
Fair value as of: Jun 25, 2026
Analysis
Shengfeng Development Limited (SFWL) currently trades at $0.8701, while our model-based Fair Value estimate is $5.63 — implying the stock looks roughly 547.1% undervalued today. We read business quality at 92/100 (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: low) — always confirm before acting.
About the company
Shengfeng Development Limited, through its subsidiaries, provides contract logistics services in the People's Republic of China. The company offers business-to-business freight transportation services, such as full truckload and less than truckload; and cloud storage services, including warehouse management, pick and pack, order fulfillment, kitting and assembly, delivery process management, in-warehouse processing, and inventory optimization management services. It also provides value-added services comprising collection on delivery, customs declaration, delivery upstairs, packaging, pay-at-arrival, return proof of delivery, and shipment protection; and software engineering, supply chain management, and technical and development services; inbound, outbound, and reverse logistics; and line-haul and short-haul distribution services. The company serves clients in various industries, including manufacturing, new energy, telecommunications, internet, fashion, fast moving consumer goods,…
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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.