China First Heavy Industries (601106) Fair Value & Analysis
Industrials · CN · Market cap 24.1B CNY
Analysis
China First Heavy Industries (601106) currently trades at ¥3.52, while our model-based Fair Value estimate is ¥3.39 — implying the stock looks roughly 3.7% overvalued today. We read business quality at 90/100 (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
China First Heavy Industries manufactures and sells technical equipment in the People's Republic of China and internationally. The company offers nuclear power equipment; petrochemical equipment, such as coal liquefaction, and hydrogenation reactors, PTA and ethylene oxide reactor, oversized heat exchangers, coal gasifiers, etc.; metallurgical equipment, including continuous casting machine, converters and electric furnaces, cold and hot strip rolling mill, sectional beam, and long product rolling mill, shearing lines, plate levelers, slab sizing presses, and coilers, etc.; forging equipment comprising mechanical, hot die forging, stretch levelers, and large hydraulic presses; and engineering equipment, such as mining excavators, shield tunneling machines, plate bending, and roller mills. It also provides spare parts and special tolls; design service; product transportation and insurance service; technical guidance and training service; product installation and debugging services; a…
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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.