Fairvalue-Calculator Fairvalue-Calculator
EN DE

Zhangjiagang Guangda Special Material Co (688186) Fair Value & Analysis

Basic Materials · CN · Market cap 4.8B CNY

Price¥16.16
Fair Value¥12.48
Upside-22.8%
Quality90/100
Evidence: High Range ¥9.36 – ¥16.80

Analysis

Zhangjiagang Guangda Special Material Co (688186) currently trades at ¥16.16, while our model-based Fair Value estimate is ¥12.48 — implying the stock looks roughly 22.8% overvalued today. We read business quality at 90/100 (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: high).

About the company

Zhangjiagang Guangda Special Material Co., Ltd. researches, develops, produces, and sells steel materials and new energy wind power components in China and internationally. It offers wind power equipment parts, including wind turbine spindles; structural parts of gearbox, hub, and body frames; special stainless steel; high-temperature alloys, corrosion resistant alloys, and steel; die and tool steel products, such as plastic mold, and hot and cold work tool steels; gear steel products; and other precision mechanical parts used in wind power, chemical engineering, machinery, automobile, vessel, power station, metallurgy, mining, and petrochemical industries. The company was founded in 2006 and is headquartered in Zhangjiagang, China.

Open the full interactive analysis →

Similar stocks

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.