Jiangsu Newamstar Packaging Machinery Co (300509) Fair Value & Analysis
Industrials · CN · Market cap 2.7B CNY
Fair value as of: Jun 24, 2026
Analysis
Jiangsu Newamstar Packaging Machinery Co (300509) currently trades at ¥8.97, while our model-based Fair Value estimate is ¥8.75 — implying the stock looks roughly 2.5% overvalued today. We read business quality at 94/100 (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
Jiangsu Newamstar Packaging Machinery Co.,Ltd engages in the research and development, manufacturing, and sale of beverage packaging machinery in China and internationally. The company offers pretreatment systems, including water treatment, processing, UHT, CIP, COP/SOP, and disinfectant blending systems, as well as carbon mixers; Starbloc, bottled water, big container, aseptic, ultra-clean, CSD, hot-fill, and non-beverage combiblocks; blowing systems, such as bottle, container, and gallon blowers; and aseptic, ultra-clean, hot, water, gallon-water, CSD, liquor, condiment, edible oil, and daily chemical product filling systems. It also provides secondary packaging systems comprising conveyor systems, bottle warmers, bottle tilting systems, labelers, shower cooling tunnels, film and carton wrappers, starpacks, sorting robots, robot encasers and palletizers, and mechanical palletizers; and automated warehouse systems, rail guided vehicles, automated guided vehicles, and warehouse mana…
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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.