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Beijing Fengjing Automotive Parts Co (002662) Fair Value & Analysis

Consumer Cyclical · CN · Market cap 5.3B CNY

Price¥3.96
Fair Value¥3.17
Upside-19.9%
Quality95/100
Evidence: Medium Range ¥2.74 – ¥3.59

Analysis

Beijing Fengjing Automotive Parts Co (002662) currently trades at ¥3.96, while our model-based Fair Value estimate is ¥3.17 — implying the stock looks roughly 19.9% 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: medium).

About the company

Beijing Fengjing Automotive Parts Co., Ltd., together with its subsidiaries, develops, manufactures, and sells interior and exterior trim systems for passenger cars in China and internationally. It operates through Exterior Decoration Parts, Interior Decoration Parts, and Other Products and Services segments. The company offers door weatherstripping, door frame strips, pillar trim, and windshield trim; center console assemblies, center console trim frames, door handle trim frames, and ashtray covers; and grilles. It also provides automotive interior and exterior decorative parts; car doors; and roof racks. The company was formerly known as Beijing WKW Automotive Parts Co.,Ltd. and changed its name to Beijing Fengjing Automotive Parts Co., Ltd. in April 2025. The company was founded in 2002 and is headquartered in Beijing, China.

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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.