PT Bank China Construction Bank Indonesia Tbk (MCOR) Fair Value & Analysis
Financial Services · ID · Market cap 2.4T IDR
Fair value as of: Jun 26, 2026
Analysis
PT Bank China Construction Bank Indonesia Tbk (MCOR) currently trades at 66.00 IDR, while our model-based Fair Value estimate is 103.52 IDR — implying the stock looks roughly 56.8% undervalued today. We read business quality at 95/100 (high quality), in the Financial Services 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: high) — always confirm before acting.
About the company
PT Bank China Construction Bank Indonesia Tbk provides various banking products and services in Indonesia. It operates through the Loans, Treasury, and Trade Finance segments. The company offers rupiah and forex deposits, such as IDR and FX time deposits; current and savings accounts; consumer, commercial, and SME loans; and safe deposit boxes, treasury and international banking, and trade finance services. It also provides ATM services and mobile banking, as well as internet banking for individuals and corporates. The company was formerly known as PT Bank Windu Kentjana International Tbk and changed its name to PT Bank China Construction Bank Indonesia Tbk in December 2016. The company was founded in 1974 and is headquartered in Jakarta Pusat, Indonesia. PT Bank China Construction Bank Indonesia Tbk is a subsidiary of China Construction Bank Corporation.
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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.