China Testing & Certification International Group (603060) Fair Value & Analysis
Industrials · CN · Market cap 6.3B CNY
Analysis
China Testing & Certification International Group (603060) currently trades at ¥6.74, while our model-based Fair Value estimate is ¥3.05 — implying the stock looks roughly 54.7% overvalued today. We read business quality at 80/100 (high quality), in the Industrials 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: medium).
About the company
China Testing & Certification International Group Co.,Ltd. provides inspection, testing, and certification services in the fields of building materials and construction engineering in China. The company offers engineering testing, material testing, environmental testing, food, and agricultural product testing services; product certification, including safety glass certification, porcelain tile certification, and brake linings for automobiles; voluntary product certification; management system certification and service certification services; and scientific research and technical services. It also engages in the research, development, and sale of testing instruments; provision of intelligent manufacturing services; and instrument measurement and verification business for industries, such as machinery, electronics, textiles, medicine, building materials, chemicals, testing institutions, and scientific research institutes. The company was formerly known as China Building Material Test …
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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.