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Guangzhou Sie Consulting Co (300687) Fair Value & Analysis

Technology · CN · Market cap 10.6B CNY

Price¥26.46
Fair Value¥19.46
Upside-26.5%
Quality88/100
Evidence: Low Range ¥14.60 – ¥24.33

Analysis

Guangzhou Sie Consulting Co (300687) currently trades at ¥26.46, while our model-based Fair Value estimate is ¥19.46 — implying the stock looks roughly 26.5% overvalued today. We read business quality at 88/100 (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

Guangzhou Sie Consulting Co., Ltd. operates as a service provider for enterprise digital-intelligent transformation in China. It offers enterprise-grade AI; smart manufacturing, supply chain, human resource, marketing, middle platform, and finance products and services; agency implementation services; intelligent research and development and ERP services; and industrial internet platform services. The company also provides professional, SIE, implementing, operational, consulting, technical, and other services. It serves electronics, petrochemical, rail traffic, trade, home furnishing, motor-dom, semiconductor, energy, financial, tourism hotel, new energy, pharmaceuticals, telecommunications, metallurgical, FMCG, elevator, logistics, medical devices, aviation, cultural and media, home appliances, internet, and pipe industries. Guangzhou Sie Consulting Co., Ltd. was incorporated in 2005 and is headquartered in Guangzhou, China.

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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.