PT Chandra Asri Pacific Tbk (PTCAY) Fair Value & Analysis
Basic Materials · US · Market cap $7.1B
Fair value as of: Jun 24, 2026
Analysis
PT Chandra Asri Pacific Tbk (PTCAY) currently trades at $8.00, while our model-based Fair Value estimate is $14.16 — implying the stock looks roughly 77.0% undervalued today. We read business quality at 84/100 (high quality), in the Basic Materials 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: medium) — always confirm before acting.
About the company
PT Chandra Asri Pacific Tbk provides chemical and infrastructure solutions in Indonesia and Singapore. It operates through Energy, Chemical, and Infrastructure segments. The company offers olefins, such as ethylene, propylene, pyrolysis gasoline, crude C4, and pyrolysis fuel oil. It also provides polyolefins comprising polyethylene products, including linear low density and high-density polyethylene under the Asrene brand name; and polypropylene products, such as homopolymer, random and impact copolymer, and terpolymer under the Trilene brand name for use in food packaging, housewares, electronics, and automotive products. The company offers styrene monomer, an organic monomer aromatics compound for use in the production of polystyrene, expanded polystyrene, styrene acrylonitrile, acrylonitrile butadiene styrene, styrene butadiene rubber, styrene butadiene latex, and unsaturated polyester resin; butadiene, a conjugated diene to produce styrene-butadiene rubber, adhesives, sealants, …
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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.