Ganfeng Lithium Group (GNENY) Fair Value & Analysis
Basic Materials · US · Market cap $15.7B
Analysis
Ganfeng Lithium Group (GNENY) currently trades at $7.49, while our model-based Fair Value estimate is $3.19 — implying the stock looks roughly 57.4% overvalued today. We read business quality at 86/100 (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
Ganfeng Lithium Group Co., Ltd. manufactures and sells lithium products in China. The company offers lithium hydroxide, carbonate, fluoride, chloride, and other compounds; lithium metals, battery grade lithium metal, lithium foil, and other lithium alloys; cesium and rubidium compounds; and Organo lithium compounds. It also provides power battery, energy storage system, 3C digital batteries, consumer electronics battery, and solid state lithium batteries; lithium battery materials, such as precursor materials and materials for solid state lithium batteries; and battery recycling solutions. In addition, the company offers raw materials to automotive, battery, and materials manufacturers; energy storage equipment for clean energy sources, including solar and wind power; battery solutions for consumer electronic devices, such as mobile phones, headphones, and robot vacuum cleaners; and lithium-ion battery systems for industrial vehicles, as well as engages in recycling, processing, and…
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.