Vanjee Technology Co (300552) Fair Value & Analysis
Technology · CN · Market cap 5.7B CNY
Analysis
Vanjee Technology Co (300552) currently trades at ¥25.09, while our model-based Fair Value estimate is ¥24.80 — implying the stock looks roughly 1.2% 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
Vanjee Technology Co., Ltd. provides intelligent transportation systems in China. It offers VanJee LiDAR WLR-711, a traffic scanner to provide data for road authority, including vehicle dimension measurement, vehicle classification, and traffic flow data collection; VanJee WLR-733, a mechanical 64-line roadside LiDAR; VanJee LiDAR WLR-716 Mini, a Mini Slam Navigation LiDAR; VanJee LiDAR WLR-718H, a short-range 2D LiDAR y for I/O anti-collision functions; VanJee LiDAR WLR-719, a long-range and high-precision SLAM navigation LiDAR; VanJee LiDAR WLR-720, a 16-Line mechanical LiDAR; VanJee LiDAR WLR-722, a 32-line spherical LiDAR; VanJee LiDAR WLR-760, an automotive-grade 192-Line LiDAR; VanJee VANJEE WLR-750 blind-spot LiDAR; and VanJee LiDAR WLR-719E, a 3D safety LiDAR. The company also provides digital strain gauge sensor; automotive electronics; and VanJee ETC RSU WER13-01, a roadside unit that can eliminate problem of vehicle in adjacent channel interference; and VanJee ETC OBU W-1…
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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.