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Jiangsu Xinri E-Vehicle Co (603787) Fair Value & Analysis

Consumer Cyclical · CN · Market cap 2.3B CNY

Price¥9.70
Fair Value¥4.75
Upside-51.0%
Quality95/100
Evidence: High Range ¥3.63 – ¥4.98

Fair value as of: Jun 24, 2026

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Analysis

Jiangsu Xinri E-Vehicle Co (603787) currently trades at ¥9.70, while our model-based Fair Value estimate is ¥4.75 — implying the stock looks roughly 51.0% 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

Jiangsu Xinri E-Vehicle Co., Ltd. researches, develops, manufactures, and sells electric vehicles in China and internationally. The company offers MIKU, utility, high speed, moped, small size, bicycle, and E-car related products. It exports its products. The company was founded in 1999 and is based in Wuxi, China.

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Frequently asked questions

Is Jiangsu Xinri E-Vehicle Co (603787) undervalued?
As of Jun 24, 2026, our model estimates a fair value of ¥4.75 versus a price of ¥9.70 — about −51% (overvalued). Model-based estimate, not financial advice.
What is the fair value of 603787?
Our 21-model fair value for Jiangsu Xinri E-Vehicle Co is ¥4.75 (as of Jun 24, 2026), built from audited fundamentals. The current price is ¥9.70.
What is the quality score of 603787?
Jiangsu Xinri E-Vehicle Co has a Quality Score of 95/100, measuring profitability, growth and balance-sheet strength from non-valuation factors.

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.