Chongqing Fenghwa Group (600615) Fair Value & Analysis
Industrials · CN · Market cap 2.1B CNY
Fair value as of: Jun 24, 2026
Analysis
Chongqing Fenghwa Group (600615) currently trades at ¥10.37, while our model-based Fair Value estimate is ¥3.77 — implying the stock looks roughly 63.6% overvalued today. We read business quality at 87/100 (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
Chongqing Fenghwa Group Co., Ltd. through its subsidiary, manufactures and sells magnesium and aluminum metal auto parts in China. It offers magnesium, aluminum alloy die castings such as automobile steering wheel frames, automobile seat slides, motorcycle parts, garden hand tools, home and office products, wall panels, new partition screens, home improvement wardrobes, cabinets, all-aluminum home customization, and bathroom cabinets, municipal products, etc. The company also engages in design, manufacture and sale of agricultural machinery, general machinery and garden machinery. The company was formerly known as Shanghai Fenghwa Group Co., Ltd. and changed its name to Chongqing Fenghwa Group Co., Ltd. in September 2022. Chongqing Fenghwa Group Co., Ltd. was incorporated in 1992 and is headquartered in Shanghai, China.
Open the full interactive analysis →
Similar stocks
Frequently asked questions
Is Chongqing Fenghwa Group (600615) undervalued?
What is the fair value of 600615?
What is the quality score of 600615?
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.