Changzhou Evergreen Technology Co (001324) Fair Value & Analysis
Industrials · CN · Market cap 2.5B CNY
Fair value as of: Jun 25, 2026
Analysis
Changzhou Evergreen Technology Co (001324) currently trades at ¥18.80, while our model-based Fair Value estimate is ¥6.23 — implying the stock looks roughly 66.9% 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: medium).
About the company
Changzhou Evergreen Technology Co., Ltd. provides solutions for the interior and exterior ornamentation of rail transit vehicles and buildings in China and internationally. It offers aluminum and stone honeycomb panels, aluminum honeycomb composite insulation board assembly curtain walls, and assembled curtain wall with composite thermal insulation boards for workshop; and indoor assembly type aluminum honeycomb composite ceiling and wall panel systems, indoor assembly type aluminum honeycomb composite partition wall and hospital prefabricated built-in integrated systems, decorative application of aluminum honeycomb composite panels in elevator, and decorative applications of aluminum honeycomb composite panel in furniture and doors. The company also provides painting and Han culture painting modules; honeycomb, modular, electric heating, foam, glued, equipment compartment, and phenolic floors; and information displays, electrical cabinets, luggage cabinets, tea and dining tables, g…
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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.