Lanzhou LS Heavy Equipment Co (603169) Fair Value & Analysis
Industrials · CN · Market cap 9.6B CNY
Fair value as of: Jun 24, 2026
Analysis
Lanzhou LS Heavy Equipment Co (603169) currently trades at ¥8.02, while our model-based Fair Value estimate is ¥9.21 — implying the stock looks roughly 14.8% undervalued today. We read business quality at 93/100 (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
Lanzhou LS Heavy Equipment Co., Ltd engages in the research and development, design, manufacture, engineering, and maintenance services of traditional energy and chemical, new energy, industrial intelligent equipment, and energy-saving and environmental protection equipment in China and internationally. The company offers nuclear chemical equipment, nuclear fuel storage and transportation containers, and chamber equipment. It also provides pressure vessel, reforming reactor, hydrogenation reactor, threaded locking heat exchanger, diaphragm heat exchanger, high pressure vessel, circulating hydrogen desulfurization towers, etc. used in oil refining field; high-pressure tube reactors, large towers, etc. used in chemical industry; gasifiers, fischer-tropsch reactors, conversion furnaces, water scrubber, intermediate heat exchangers, waste heat boilers, used in coal chemical industry; reactors, vaporizers, reactors, coolers, and special material containers used in fine chemical industry.…
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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.