Shenzhen Anche Technologies Co (300572) Fair Value & Analysis
Technology · CN · Market cap 6.5B CNY
Analysis
Shenzhen Anche Technologies Co (300572) currently trades at ¥26.90, while our model-based Fair Value estimate is ¥21.15 — implying the stock looks roughly 21.4% overvalued today. We read business quality at 94/100 (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
Shenzhen Anche Technologies Co., Ltd. provides motor vehicle inspection solutions in China. The company offers motor vehicle inspection systems, inspection industry (networking) supervision systems, and motor vehicle inspection services. It also provides tester products, including roller and plate brake testers, suspension testers, car and truck side slip testers, speedometer testers, emission testers, and headlight testers. In addition, the company offers chassis dynamometers, PN counters, tire tread depth measuring devices, play detectors, and motorcycle test lane products. Further, it provides technical solutions, including electric vehicle test system, vehicle inspection industry supervision platform, vehicle end-of-line test system, and driving test system. Additionally, the company offers vehicle remote sensing test system, new energy vehicle test system, electric vehicle safety inspections, used car assessment system, and a 3-ton side slip tester. Shenzhen Anche Technologies …
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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.