Fuyao Glass Industry Group (FIGIF) Fair Value & Analysis
Consumer Cyclical · US · Market cap $26.1B
Analysis
Fuyao Glass Industry Group (FIGIF) currently trades at $10.00, while our model-based Fair Value estimate is $9.40 — implying the stock looks roughly 6.0% overvalued today. We read business quality at 95/100 (high quality), in the Consumer Cyclical 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
Fuyao Glass Industry Group Co., Ltd., together with its subsidiaries, provides safety glass solutions and automotive accessories for various transportation vehicles in China and internationally. It designs, manufactures, sells, and services automotive float glass, automotive glass, locomotive glass, motorcycle glass, luggage racks, and window accessories. The company's glass products include smart and panoramic skylight glass, dimmable glass, head-up display glass, coated heatable glass, and flush mounted tempered laminated glass. It is also involved in property leasing; manufacture of machinery; exploitation and sale of minerals; production and sale of mold, automotive parts, fiber products, plastic products, automotive decoration, and refined aluminums; installation of automotive glass accessories; and technology transfer, technical services, and technology promotion. In addition, it offers warehousing and automotive glass logistics services. The company was formerly known as Fuji…
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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.