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Wankai New Materials Co (301216) Fair Value & Analysis

Basic Materials · CN · Market cap 14.3B CNY

Price¥24.75
Fair Value¥4.95
Upside-80.0%
Quality95/100
Evidence: Medium Range ¥3.72 – ¥6.19

Analysis

Wankai New Materials Co (301216) currently trades at ¥24.75, while our model-based Fair Value estimate is ¥4.95 — implying the stock looks roughly 80.0% overvalued today. We read business quality at 95/100 (high quality), in the Basic Materials 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

Wankai New Materials Co., Ltd. engages in the research, development, production, and sale of polyethylene terephthalate (PET) materials in China and internationally. The company offers bottle-grade PET chips used in the production of bottle packaging containers, such as water bottles, beverage bottles, edible oil bottles, and cosmetic bottles, etc. It also provides non-bottle-grade PET chips, including super bright PET for BOPET film, PET polymer to produce fiber or filament yarn, and polyester PET chips for producing photovoltaic back films. In addition, the company offers high-brightness PET resins, PETG resins for heat shrink films and PETG sheets, polyester resins for injection molding, and PCR-PET resins. The company was formerly known as Zhejiang Wankai New Materials Co., Ltd. The company was founded in 2008 and is headquartered in Jiaxing, 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.