CMOC Group (CMCLY) Fair Value & Analysis
Basic Materials · US · Market cap $74.4B
Analysis
CMOC Group (CMCLY) currently trades at $148.88, while our model-based Fair Value estimate is $119.76 — implying the stock looks roughly 19.6% overvalued today. We read business quality at 78/100 (high quality), in the Basic Materials 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
CMOC Group Limited, together with its subsidiaries, engages in the mining, beneficiation, and smelting of base and rare metals in Asia, Africa, South America, and Europe. It operates through Molybdenum and Tungsten Related Products; Niobium and Phosphorus Related Products; Copper-Cobalt Related Products; Mineral and Metal Trade; Refined Metals Trade; and Others segments. The company offers copper, gold, cobalt, molybdenum, tungsten, and niobium; and phosphate fertilizer. It also engages in trading in base metals, and molybdenum and tungstenic products; selection, processing, refining, and sale of mineral products; import and export of goods and technology; consulting; asset and investment management; enterprise operation and management; technology services; logistics transportation; technical services and software development; and hotel operation activities. The company was formerly known as China Molybdenum Co., Ltd. and changed its name to CMOC Group Limited in June 2022. CMOC Gro…
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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.