PT Astra International Tbk, (PTAIF) Fair Value & Analysis
Industrials · US · Market cap $11.0B
Fair value as of: Jun 26, 2026
Analysis
PT Astra International Tbk, (PTAIF) currently trades at $0.2747, while our model-based Fair Value estimate is $0.5500 — implying the stock looks roughly 100.2% undervalued today. We read business quality at 80/100 (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: medium) — always confirm before acting.
About the company
PT Astra International Tbk, together with its subsidiaries, operates in the automotive, financial services, heavy equipment, mining, construction, energy, agri, infrastructure and logistics, information technology, and property businesses in Indonesia and internationally. It operates in Automotive, Financial Services, Heavy Equipment and Mining, construction, and energy, infrastructure and logistics, Agribusiness, Information Technology, and Property segments. The Automotive segment includes car, motorcycle, component, and others. Its Financial Services segment engages in car, motorcycle, heavy equipment financing, and Insurance. The Heavy Equipment and Mining, construction, and energy segment engages in construction machines, mining contractor, mining, and construction. Its infrastructure and logistics segment includes infrastructure and logistics value chain services; Agribusiness segment include oil palm plantations and their derivative products. The Information Technology segmen…
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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.