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RongFa Nuclear Equipment Co (002366) Fair Value & Analysis

Industrials · CN · Market cap 13.1B CNY

Price¥5.90
Fair Value¥9.86
Upside+67.1%
Quality86/100
Evidence: Low Range ¥7.39 – ¥12.32

Analysis

RongFa Nuclear Equipment Co (002366) currently trades at ¥5.90, while our model-based Fair Value estimate is ¥9.86 — implying the stock looks roughly 67.1% undervalued today. We read business quality at 86/100 (high quality), in the Industrials sector. Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: low) — always confirm before acting.

About the company

RongFa Nuclear Equipment Co., Ltd., through its subsidiaries, engages in the manufacturing of high-end equipment and machinery and minerals in China and internationally. The company offers nuclear power, reactor coolant circulation main pump casing, container and valve box forgings, nuclear power castings, petrochemical equipment, and large ring rolling products. It provides technical equipment, high-tech components, and technical services for nuclear power, energy, electric power, petrochemical, metallurgy, national defense, and other industries. The company was formerly known as Taihai Manoir Nuclear Equipment Co., Ltd. and changed its name to RongFa Nuclear Equipment Co., Ltd. in June 2023. RongFa Nuclear Equipment Co., Ltd. was founded in 1997 and is headquartered in Yantai, China. RongFa Nuclear Equipment Co., Ltd. is a subsidiary of Yantai Taihai Group Co., Ltd.

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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.