GRG Metrology & Test Group (002967) Fair Value & Analysis
Industrials · CN · Market cap 11.6B CNY
Analysis
GRG Metrology & Test Group (002967) currently trades at ¥19.12, while our model-based Fair Value estimate is ¥13.99 — implying the stock looks roughly 26.8% overvalued today. We read business quality at 90/100 (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: high).
About the company
GRG Metrology & Test Group Co., Ltd., together with its subsidiaries, operates as a third-party measurement and testing institution in China. It offers measurement and calibration, environmental and reliability testing, EMC testing, chemical and failure analysis, safety test and certification, environmental monitoring, and food and agricultural product testing services; integrated circuit testing and analysis, electromagnetic compatibility testing, and ecological environment testing; and EHS evaluation services, including safety evaluation, environmental impact assessment, and occupational health evaluation; and reference materials. The company also provides data science analysis and evaluation services, such as network security, software evaluation, and artificial intelligence testing; and technical services, including artificial intelligence ecosystem verification and human-machine efficacy evaluation, as well as digital services comprising data governance capability evaluation, d…
Open the full interactive analysis →
Similar stocks
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.