Shandong New Beiyang Information Technology Co (002376) Fair Value & Analysis
Industrials · CN · Market cap 5.0B CNY
Analysis
Shandong New Beiyang Information Technology Co (002376) currently trades at ¥6.06, while our model-based Fair Value estimate is ¥1.86 — implying the stock looks roughly 69.3% overvalued today. We read business quality at 88/100 (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
Shandong New Beiyang Information Technology Co., Ltd. engages in the research and development, production, sale, and service of thermal printing, identification, and smart system integration products in China. The company offers smart finance, smart logistics, smart retail, and other industries solutions. It also provides portable printer, receipt printer, label printer, embedded printer, printer mechanism, and green products; document scanner, embedded scanner, and scanner; cash recycling, and movement; and card copier, and laser printer. In addition, the company offers self-service ticket, and machines; traffic police autonomous penalty system medical service system, and government service system; small telescopic machine; cross belt, balance wheel, and sorting cabinet; delivery services; DWS, static bubble counter, and portable bubble counting; express lockers, food cabinet, and cold chain cabinet; and XY vending machine, and traditional vending machines. It serves logistics, fin…
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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.