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Chengdu Yunda Technology Co (300440) Fair Value & Analysis

Industrials · CN · Market cap 8.7B CNY

Price¥18.92
Fair Value¥4.93
Upside-73.9%
Quality94/100
Evidence: High Range ¥3.54 – ¥6.32

Analysis

Chengdu Yunda Technology Co (300440) currently trades at ¥18.92, while our model-based Fair Value estimate is ¥4.93 — implying the stock looks roughly 73.9% overvalued today. We read business quality at 94/100 (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: high).

About the company

Chengdu Yunda Technology Co., Ltd., together with its subsidiaries, provides rail transit intelligent solutions and services in China. The company offers various solutions, including smart training, intelligent operation and maintenance, smart depot, smart traction power, smart freight, and smart vehicle section, as well as simulation training products. It also provides repair and maintenance for railroad rolling stock and ground equipment; overhaul of locomotive, passenger and moving cars, and metro vehicle components; agency sale of rolling stock parts, railway special equipment, special parts, and various types of consumables; technical services for various rail transportation fields; and technological change development. The company serves national railways, urban railways, and vocational colleges. Chengdu Yunda Technology Co., Ltd. was founded in 1992 and is based in Chengdu, 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.