PT Multipolar Technology Tbk (MLPT) Fair Value & Analysis
Technology · ID · Market cap 28.1T IDR
Analysis
PT Multipolar Technology Tbk (MLPT) currently trades at 18,550 IDR, while our model-based Fair Value estimate is 3,589 IDR — implying the stock looks roughly 80.7% overvalued today. We read business quality at 95/100 (high quality), in the Technology 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: high).
About the company
PT Multipolar Technology Tbk provides consultation, integration, and information technology management services in Indonesia. The company offers security platforms and services, including application, network, data, user, managed security services, and eKYC; digital insights comprising data management, artificial intelligence, big data and analytics, business performance analytics services, credit scoring API, billing payment dashboard, and fraud detection and campaign systems; and hybrid infrastructure platforms and services that include server, storage, network, hyperconverged, PC and laptops, ATM and self-service devices, cloud infrastructure, OS and virtualization services, and hybrid cloud managed services. It also provides business solution platforms and services, such as banking solutions, smart office, enterprise project, IT services management, enterprise resource planning, system development life cycle and testing management, e-channel, mobile banking platform, supply chai…
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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.