Nanhua Instruments Co (300417) Fair Value & Analysis
Technology · CN · Market cap 1.5B CNY
Fair value as of: Jun 24, 2026
Analysis
Nanhua Instruments Co (300417) currently trades at ¥11.13, while our model-based Fair Value estimate is ¥1.45 — implying the stock looks roughly 87.0% overvalued today. We read business quality at 80/100 (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: medium).
About the company
Nanhua Instruments Co., Ltd. manufactures and sells vehicle test equipment in China. The company offers vehicle inspection line products, such as dynamometer, emission analyzers, steering clearance tester, suspension, wheel steering angle, plate brake, speed meter, wheel load, side slip, break, multi-axle wheel load, vehicle dimension, and loaded brake testers, as well as free roller and play detector. It also offers exhaust gas analyzers, BASM detection products, VMAS/ASM/lugdown testing systems, and headlight testers. In addition, the company provides motor vehicle emission detection equipment, environmental protection detection systems, and safety detection instruments, as well as safety detection systems, including computer detection/management network control system software. It also exports its products to Asia, the United States, Europe, Africa, and other countries. The company was founded in 1996 and is based in Foshan, 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.