PT Asuransi Multi Artha Guna Tbk (AMAG) Fair Value & Analysis
Financial Services · ID · Market cap 1.8T IDR
Analysis
PT Asuransi Multi Artha Guna Tbk (AMAG) currently trades at 400.00 IDR, while our model-based Fair Value estimate is 411.33 IDR — implying the stock looks roughly 2.8% 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 Asuransi Multi Artha Guna Tbk provides general insurance in Indonesia. It operates through Motor Vehicles, Health, Fire, and Other segments. The company offers MAGNA car protection that covers loss or damage to the car, which includes the value of the vehicle or its equipment; MAGNA Sehat, which provides healthcare coverage and hospitalization; MAGNA Properti that covers loss or damage to the insured property directly caused by fire, lightning, explosion, airplane crash and smoke; MAGNA Wisata Overseas, an international travel insurance; and MAGNA Wisata Domestik, a domestic travel insurance that covers personal accident during the trip, medical expenses, lost or delayed baggage, protection of personal money, travel documents, and legal liability to third parties. It also provides cyber risk, personal accident, health, money, property/industrial all risk, earthquake, engineering, heavy equipment, marine cargo, and burglary insurances; and other special products comprising insuran…
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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.